Mask Hysteria

A few days ago , people in a Staten Island grocery store collectively shrieked in panicked terror because a fellow shopper was not wearing a surgical mask .
Hysteria quickly ensued , with masked shoppers forming a threatening mob , as some howled , “ GET OUT !
GET OUT !
” and driving the non-masked deviant away from the store .
Watching the video on Twitter ( of course ! )
was like seeing people in the Old Testament shrieking “ Unclean !
Unclean !
” at an approaching leper .
Were these terrified people justified in ejecting the @ @ @ @ @ @ @ means of peer pressure to wear a mask ?
Joe Biden ’ s chief health adviser , the bioethicist Ezekiel Emanuel , would probably say yes .
He has argued in the New York Times that mask-wearing should be universal and imposed by law .
In a co-authored piece , he wrote , The most obvious path to universal masking is to pass laws and punish infractions .
But enforcing legal edicts to wear masks in public can be difficult and costly , and amid widespread ambivalence can lead to backlash and even @ @ @ @ @ @ @ complete solution .
As experts in public health and human behavior , we propose a complementary approach : Make wearing a mask easy , understood and expected .
Don ’ t be fooled by the deployment of passive language .
Not a “ complete solution , ” means that legal mandates are part of the answer to compelling mask-wearing .
Taking “ complimentary ” action means in addition to — not instead of — passing laws .
To make us all more likely to obey mask diktats from our COVID overlords , the @ @ @ @ @ @ @ socially engineer a new “ norm ” that would have us wear masks at all times we are in public places .
But does Gwyneth Paltrow Instagramming a photo of herself wearing a mask really influence individual behavior ?
Good grief , I hope our culture is not that shallow !
Meanwhile , Virginia ’ s hapless Gov .
Ralph Northam , of blackface infamy — photographed not wearing a mask or social distancing at a beach recently — just issued a directive requiring everyone to wear masks at all times in public @ @ @ @ @ @ @ such as when exercising ) .
From the VPM.org story : Not surprisingly , in our divided times , masks have become political , with Democrats more likely to insist on being masked than Republicans .
Sometimes , one even sees people wearing masks driving alone in a car .
On the other end of the spectrum , people have refused to wear masks as a supposed act of patriotism , which certainly seems dubious .
And of course , we mustn ’ t forget the hypocrisy factor as media scolds like CNN @ @ @ @ @ @ @ Pence on wearing masks — only to be busted for being seen in public sans mask while actively infected with the disease .
Amidst this Sturm und Drang , a question begs to be asked : Does wearing a mask generally prevent the wearer from catching or spreading COVID-19 ?
If there is going to be a mandate , that should be the necessary predicate to imposing it , right ?
And considering the ruling class , media , and celebrities ’ insistence that we are sinners if we go maskless in public @ @ @ @ @ @ @ must be yes .
No .
One would be wrong .
Wait , what ?
But the science !
Exactly .
Let ’ s look at the science .
According to a new article published in the New England Journal of Medicine ( NEJM ) , in public contexts outside of hospitals and other health-care settings , masks offer the wearer and those with whom he or she comes into contact scarce preventive benefit .
From “ Universal Masking in Hospitals in the Covid-19 Era ” ( my emphasis ) : In other @ @ @ @ @ @ @ were terrified without a cause .
Humiliating and driving that poor maskless woman out of the store did not protect their , her , or others ’ health .
Back to the NEJM article .
Even in health-care settings , where people are more likely to cross paths with infected patients , “ a mask alone … will reduce the risk only slightly ” because The authors do write that in the context of working for hours a day in a health-care facility , universal masking “ might limit transmission ” from asymptomatic @ @ @ @ @ @ @ But even then , “ mask wearing is not a panacea ” and will not protect providers “ if it is not accompanied by meticulous hand hygiene , eye protection , gloves , and a gown.
” But that is not a circumstance faced by the general public .
This is not to say there are not non-medical benefits potentially attained by mask wearing .
The authors write , But does a subliminal emotional benefit justify legal mandates and socially imposed compliance with masking ?
No .
Based on this scientific article , @ @ @ @ @ @ @ is not medically necessary in most public settings .
So why the continual pressure for masking ?
I suspect that mandatory edicts such as Northam ’ s and advocacy such as Emanuel ’ s are more about imposing social control than actually preventing disease .
This is not to say that stores are wrong to require shoppers to mask up , since people may be more willing to enter the premises knowing that everyone ’ s mouth and nose will be covered .
And certainly it does not mean that as a matter @ @ @ @ @ @ @ that we should not voluntarily don our face coverings .
Walk the extra mile , and all that .
So let ’ s socially distance from each other in the proper manner and maintain proper hand washing and other hygiene standards .
But legally or socially penalize those who go barefaced in casual or non-close quarters public settings ?
Absolutely not .
The science does not warrant imposing that kind of coercive “ norm .
”

A few days ago, people in a Staten Island grocery store collectively shrieked in panicked terror because a fellow shopper was not wearing a surgical mask. Hysteria quickly ensued, with masked shoppers forming a threatening mob, as some howled, “GET OUT! GET OUT!” and driving the non-masked deviant away from the store. Watching the video on Twitter (of course!) was like seeing people in the Old Testament shrieking “Unclean! Unclean!” at an approaching leper.
Were these terrified people justified in ejecting the maskless shopper from their midst as a means of peer pressure to wear a mask? Joe Biden’s chief health adviser, the bioethicist Ezekiel Emanuel, would probably say yes. He has argued in the New York Times that mask-wearing should be universal and imposed by law. In a co-authored piece, he wrote,
The most obvious path to universal masking is to pass laws and punish infractions. But enforcing legal edicts to wear masks in public can be difficult and costly, and amid widespread ambivalence can lead to backlash and even violence. So edicts are not a complete solution. As experts in public health and human behavior, we propose a complementary approach: Make wearing a mask easy, understood and expected.
Don’t be fooled by the deployment of passive language. Not a “complete solution,” means that legal mandates are part of the answer to compelling mask-wearing. Taking “complimentary” action means in addition to — not instead of — passing laws.
To make us all more likely to obey mask diktats from our COVID overlords, the technocrats urge opinion molders and celebrities to socially engineer a new “norm” that would have us wear masks at all times we are in public places. But does Gwyneth Paltrow Instagramming a photo of herself wearing a mask really influence individual behavior? Good grief, I hope our culture is not that shallow!
Meanwhile, Virginia’s hapless Gov. Ralph Northam, of blackface infamy — photographed not wearing a mask or social distancing at a beach recently — just issued a directive requiring everyone to wear masks at all times in public places (with a few exceptions, such as when exercising). From the VPM.org story:
Despite criticism for his maskless appearance on the beach, Northam announced mandatory masks in public starting this Friday for all people, including children 10 and up. This will include in stores, restaurants, and public transportation. There will be exceptions for eating, drinking, exercising, and for anyone who can’t wear a mask due to medical reasons. Masks may be home-made, but Northam recommends all Virginians, including children over the age of three, should wear masks in public.
Not surprisingly, in our divided times, masks have become political, with Democrats more likely to insist on being masked than Republicans. Sometimes, one even sees people wearing masks driving alone in a car. On the other end of the spectrum, people have refused to wear masks as a supposed act of patriotism, which certainly seems dubious. And of course, we mustn’t forget the hypocrisy factor as media scolds like CNN anchor Chris Cuomo lecturing Vice President Mike Pence on wearing masks — only to be busted for being seen in public sans mask while actively infected with the disease.
Amidst this Sturm und Drang, a question begs to be asked: Does wearing a mask generally prevent the wearer from catching or spreading COVID-19? If there is going to be a mandate, that should be the necessary predicate to imposing it, right? And considering the ruling class, media, and celebrities’ insistence that we are sinners if we go maskless in public, one would think that the answer must be yes.
No. One would be wrong.
Wait, what? But the science!
Exactly. Let’s look at the science. According to a new article published in the New England Journal of Medicine (NEJM), in public contexts outside of hospitals and other health-care settings, masks offer the wearer and those with whom he or she comes into contact scarce preventive benefit. From “Universal Masking in Hospitals in the Covid-19 Era” (my emphasis):
We know that wearing a mask outside health care facilities offers little, if any, protection from infection. Public health authorities define a significant exposure to Covid-19 as face-to-face contact within 6 feet with a patient with symptomatic Covid-19 that is sustained for at least a few minutes (and some say more than 10 minutes or even 30 minutes). The chance of catching Covid-19 from a passing interaction in a public space is therefore minimal.
In other words, the frenzied Staten Island shoppers were terrified without a cause. Humiliating and driving that poor maskless woman out of the store did not protect their, her, or others’ health.
Back to the NEJM article. Even in health-care settings, where people are more likely to cross paths with infected patients, “a mask alone … will reduce the risk only slightly” because
[i]t does not provide protection from droplets that may enter the eyes or from fomites [objects such as clothes or utensils likely to carry infection] on the patient or in the environment that providers may pick up on their hands and carry to their mucus membranes (particularly given the concern that mask wearers may have an increased tendency to touch their faces).
The authors do write that in the context of working for hours a day in a health-care facility, universal masking “might limit transmission” from asymptomatic or minimally symptomatic fellow health-care workers. But even then, “mask wearing is not a panacea” and will not protect providers “if it is not accompanied by meticulous hand hygiene, eye protection, gloves, and a gown.” But that is not a circumstance faced by the general public.
This is not to say there are not non-medical benefits potentially attained by mask wearing. The authors write,
Masks are visible reminders of an otherwise invisible yet widely prevalent pathogen and may remind people of the importance of social distancing and other infection-control measures. It is also clear that masks serve symbolic roles. Masks are not only tools, they are also talismans that may help increase health care workers’ perceived sense of safety, well-being, and trust in their hospitals. Although such reactions may not be strictly logical, we are all subject to fear and anxiety, especially during times of crisis.… Expanded masking protocols’ greatest contribution may be to reduce the transmission of anxiety, over and above whatever role they may play in reducing transmission of Covid-19.
But does a subliminal emotional benefit justify legal mandates and socially imposed compliance with masking? No. Based on this scientific article, legally requiring us to all wear masks is not medically necessary in most public settings. So why the continual pressure for masking? I suspect that mandatory edicts such as Northam’s and advocacy such as Emanuel’s are more about imposing social control than actually preventing disease.
This is not to say that stores are wrong to require shoppers to mask up, since people may be more willing to enter the premises knowing that everyone’s mouth and nose will be covered. And certainly it does not mean that as a matter of courtesy and comforting the fearful, that we should not voluntarily don our face coverings. Walk the extra mile, and all that. So let’s socially distance from each other in the proper manner and maintain proper hand washing and other hygiene standards. But legally or socially penalize those who go barefaced in casual or non-close quarters public settings? Absolutely not. The science does not warrant imposing that kind of coercive “norm.”


discoveryinstitute--2020-06-03--Does COVID-19 Lead Women to Cheat?

Does COVID-19 Lead Women to Cheat?

Is it really true that , as a recent column in a psychology magazine claims , women are more likely to cheat during the COVID-19 crisis ?
An evolutionary psychologist thinks so ; let ’ s explore this a bit .
First , philosopher Subrena E. Smith ( right ) has recently pointed out that evolutionary psychology ( EP ) is a doubtful enterprise in science , maybe an “ impossible ” one .
EP ’ s basis is modern folklore : The claim that we inherited modules from the Stone Age that govern our behavior with respect to “ @ @ @ @ @ @ @ cheater detection ” — which sold paperbacks in the 1980s — do not correlate with neuroscience findings about the human brain .
We have not found any such modules .
We have no way of knowing whether the neural correlates of our behavior are the same as those of humans who lived under very different circumstances 50,000 years ago .
Neuroplasticity ( the tendency of brains to change with types of use ) raises doubts .
Brains don ’ t fossilize .
Smith calls this lack of clear correlation the “ Matching Problem.
@ @ @ @ @ @ @ the behavior of , say , gorillas .
We have no reason to believe that they behave differently today from the way they did in the human Stone Age .
Humans , by contrast , have a detailed and varied history since then , which includes trips to the Moon .
Something happened to us that did not happen to them .
Smith makes an important contribution to the debate by identifying the “ subpersonal ” approach of evolutionary psychology , an approach that deforms and misdirects discussions .
The evolutionary psychologist melds @ @ @ @ @ @ @ animals can obviously know into a huge , confusing mess .
Explicitly , EP seems to deny the assumption that abstract thinking makes any difference to human behavior .
But of course it does and it must .
Consider , as noted earlier , issues around “ fatherhood , ” Perhaps the gorilla is driven by some ( so far unidentified ) inner force to spread his selfish genes .
That might explain why gorillas continue to exist .
Very well , but many factors play a role in why gorillas continue to @ @ @ @ @ @ @ are now protected by humans from other humans .
There is far more evidence for human protection of gorillas than for the selfish gene .
But we needn ’ t press the point .
What ’ s not happening with gorillas is actually more significant : A gorilla may have a sense of territory and what ’ s “ his ” but he has no abstract idea of fatherhood .
He probably does not know that baby gorillas come to exist explicitly as a result of adult gorillas ’ actions .
But he @ @ @ @ @ @ @ either .
If he never wonders , he is none the worse for it .
More important , in terms of “ fatherhood , ” he does not know that a specific baby gorilla came to exist as a result of his own actions , nor would he necessarily conclude that any obligations to his offspring might follow .
All this is so different from the typical human experience , played out over a vast variety of cultures around the world , that it is a wonder anyone entertains “ evo psych ” @ @ @ @ @ @ @ awareness radically changes what we experience .
We encounter this problem in a number of areas in which evolutionary psychology proposes explanations for human behavior .
Consider our title question , sexual infidelity ( “ cheating ” ) .
The recent column by Martin Graff ( left ) in a psychology magazine , offering an “ evolutionary perspective ” on female sexual infidelity during the COVID-19 crisis , tells us : Genetic immunity from disease is obviously necessary for the wellbeing of a women ’ s offspring , yet because there are no @ @ @ @ @ @ @ functioning in men , beyond perhaps appearing to be in good health , it may be difficult for women to select men on this criterion .
The only way in which women can attempt to increase the probability of strong immunity in their offspring in a time of infection in the environment , is to engage in a multi-male mating strategy , which would provide the genetic diversity afforded when their children are fathered by different men , and make it more likely that at least some offspring will possess the necessary genetic @ @ @ @ @ @ @ the threat of disease in the environment may increase the likelihood of women seeking sexually diverse mating strategies driven by such bet-hedging behaviour .
Martin Graff , “ Will the Coronavirus Threat Lead to Female Infidelity ?
” at Psychology Today While Dr. Graff claims that some studies back his assertion , it sounds so off-base that one can only suspect that a poorly supported psychology underlies such thinking .
Women ’ s experience of motherhood is , obviously , different from men ’ s experience of fatherhood .
For one thing , @ @ @ @ @ @ @ are a mother , you know you are one .
The mother may be planning to kill , abandon , sell , or raise the child .
But she more or less knows what she is expecting , even if she does not understand how it came about .
Because she is a human being , she is also aware of something else : Children believed to be the outcome of infidelity may have poorer survival prospects .
At the same time , there are many things she could know but maybe doesn @ @ @ @ @ @ @ and place .
Maybe she does not know the germ theory of disease or immunity .
Her culture may have some traditions of placating the forces of nature , some of which work better than others in checking disease — though , apart from science , a biology-based account of the events remains elusive .
In short , the things she knows and doesn ’ t know , have nothing to do with the evolutionary psychologist ’ s speculations about her innate drive to spread her selfish genes ( a drive only speculated @ @ @ @ @ @ @ engage in indiscriminate sexual behavior during a pandemic ( itself a risky proposition for her ) , the cause might be more conventionally ascribed to underlying stress and anxiety in the face of an unfamiliar threat .
To understand why evolutionary psychology is taken seriously , it helps to realize that the “ subpersonal ” approach to human psychology — the assumption that humans are no more able to understand or affect their environment than , say , chimpanzees or gorillas are — is favored by with naturalism in science ( nature is @ @ @ @ @ @ @ it is in conflict with evidence .
Naturalist theories of human psychology generally hold that human consciousness is an illusion that our ancestors accidentally evolved in order to survive .
On that view , there is no reason to think that consciousness impacts the way we behave .
Recently , philosopher David Papineau expressed the widely held — though not always clearly articulated — view that the notoriously Hard Problem of consciousness would disappear if we just stopped making any distinction between the mind and the brain .
Evolutionary psychology might have become @ @ @ @ @ @ @ project he espouses .
The problem is , EP appears wedded to a view of the human brain that neuroscience does not support .
Thus , proponents expend their energy dreaming up scenarios of how humans might behave if they didn ’ t really have human awareness and were guided instead by a so-far-undiscovered module in the brain which aims at producing healthy offspring .
But that ’ s not all .
A bigger problem awaits .
Neuroscience supports a different view : The mind is not identical with the brain .
It @ @ @ @ @ @ @ mind evolved at all and therefore whether evolutionary psychology is any help in understanding it .
The problem is , as noted earlier , one can not at one and the same time claim to represent science as applied to human psychology and refuse to be bound by its standards .
Philosopher flattens evolutionary psychology .
There is no such thing as a fossil mind .
Philosopher : Consciousness is not a problem .
Dualism is !
Physicalist David Papineau says consciousness is just “ brain processes that feel like something ”

Is it really true that, as a recent column in a psychology magazine claims, women are more likely to cheat during the COVID-19 crisis? An evolutionary psychologist thinks so; let’s explore this a bit.
First, philosopher Subrena E. Smith (right) has recently pointed out that evolutionary psychology (EP) is a doubtful enterprise in science, maybe an “impossible” one.
EP’s basis is modern folklore: The claim that we inherited modules from the Stone Age that govern our behavior with respect to “predator avoidance, mate selection, and cheater detection” — which sold paperbacks in the 1980s — do not correlate with neuroscience findings about the human brain. We have not found any such modules. We have no way of knowing whether the neural correlates of our behavior are the same as those of humans who lived under very different circumstances 50,000 years ago. Neuroplasticity (the tendency of brains to change with types of use) raises doubts. Brains don’t fossilize. Smith calls this lack of clear correlation the “Matching Problem.”
No similar problem exists for understanding the behavior of, say, gorillas. We have no reason to believe that they behave differently today from the way they did in the human Stone Age. Humans, by contrast, have a detailed and varied history since then, which includes trips to the Moon. Something happened to us that did not happen to them.
Smith makes an important contribution to the debate by identifying the “subpersonal” approach of evolutionary psychology, an approach that deforms and misdirects discussions. The evolutionary psychologist melds what humans can obviously know and what animals can obviously know into a huge, confusing mess. Explicitly, EP seems to deny the assumption that abstract thinking makes any difference to human behavior. But of course it does and it must.
Consider, as noted earlier, issues around “fatherhood,” Perhaps the gorilla is driven by some (so far unidentified) inner force to spread his selfish genes. That might explain why gorillas continue to exist. Very well, but many factors play a role in why gorillas continue to exist, including the fact that they are now protected by humans from other humans.
There is far more evidence for human protection of gorillas than for the selfish gene. But we needn’t press the point. What’s not happening with gorillas is actually more significant: A gorilla may have a sense of territory and what’s “his” but he has no abstract idea of fatherhood. He probably does not know that baby gorillas come to exist explicitly as a result of adult gorillas’ actions. But he isn’t wondering about such things either. If he never wonders, he is none the worse for it.
More important, in terms of “fatherhood,” he does not know that a specific baby gorilla came to exist as a result of his own actions, nor would he necessarily conclude that any obligations to his offspring might follow. All this is so different from the typical human experience, played out over a vast variety of cultures around the world, that it is a wonder anyone entertains “evo psych” explanations of fatherhood at all.
Human awareness radically changes what we experience.
We encounter this problem in a number of areas in which evolutionary psychology proposes explanations for human behavior. Consider our title question, sexual infidelity (“cheating”). The recent column by Martin Graff (left) in a psychology magazine, offering an “evolutionary perspective” on female sexual infidelity during the COVID-19 crisis, tells us:
Genetic immunity from disease is obviously necessary for the wellbeing of a women’s offspring, yet because there are no real obvious visual indications of genetic immune functioning in men, beyond perhaps appearing to be in good health, it may be difficult for women to select men on this criterion. The only way in which women can attempt to increase the probability of strong immunity in their offspring in a time of infection in the environment, is to engage in a multi-male mating strategy, which would provide the genetic diversity afforded when their children are fathered by different men, and make it more likely that at least some offspring will possess the necessary genetic immunity to protect them. Therefore, the threat of disease in the environment may increase the likelihood of women seeking sexually diverse mating strategies driven by such bet-hedging behaviour. Martin Graff, “Will the Coronavirus Threat Lead to Female Infidelity?” at Psychology Today
While Dr. Graff claims that some studies back his assertion, it sounds so off-base that one can only suspect that a poorly supported psychology underlies such thinking.
Women’s experience of motherhood is, obviously, different from men’s experience of fatherhood. For one thing, it is not remote. If you are a mother, you know you are one. The mother may be planning to kill, abandon, sell, or raise the child. But she more or less knows what she is expecting, even if she does not understand how it came about.
Because she is a human being, she is also aware of something else: Children believed to be the outcome of infidelity may have poorer survival prospects.
At the same time, there are many things she could know but maybe doesn’t, depending on her time and place. Maybe she does not know the germ theory of disease or immunity. Her culture may have some traditions of placating the forces of nature, some of which work better than others in checking disease — though, apart from science, a biology-based account of the events remains elusive.
In short, the things she knows and doesn’t know, have nothing to do with the evolutionary psychologist’s speculations about her innate drive to spread her selfish genes (a drive only speculated to exist). If she did engage in indiscriminate sexual behavior during a pandemic (itself a risky proposition for her), the cause might be more conventionally ascribed to underlying stress and anxiety in the face of an unfamiliar threat.
To understand why evolutionary psychology is taken seriously, it helps to realize that the “subpersonal” approach to human psychology — the assumption that humans are no more able to understand or affect their environment than, say, chimpanzees or gorillas are — is favored by naturalism in science (nature is all there is), even though it is in conflict with evidence.
Naturalist theories of human psychology generally hold that human consciousness is an illusion that our ancestors accidentally evolved in order to survive. On that view, there is no reason to think that consciousness impacts the way we behave. Recently, philosopher David Papineau expressed the widely held — though not always clearly articulated — view that the notoriously Hard Problem of consciousness would disappear if we just stopped making any distinction between the mind and the brain.
Evolutionary psychology might have become a vital part of the sort of project he espouses. The problem is, EP appears wedded to a view of the human brain that neuroscience does not support. Thus, proponents expend their energy dreaming up scenarios of how humans might behave if they didn’t really have human awareness and were guided instead by a so-far-undiscovered module in the brain which aims at producing healthy offspring.
But that’s not all. A bigger problem awaits. Neuroscience supports a different view: The mind is not identical with the brain. It’s an open question whether the mind evolved at all and therefore whether evolutionary psychology is any help in understanding it.
The problem is, as noted earlier, one cannot at one and the same time claim to represent science as applied to human psychology and refuse to be bound by its standards.
Further reading:
Philosopher flattens evolutionary psychology. There is no such thing as a fossil mind.
and
Philosopher: Consciousness is not a problem. Dualism is! Physicalist David Papineau says consciousness is just “brain processes that feel like something”


discoveryinstitute--2020-06-03--Racism Violates Human Exceptionalism

Racism Violates Human Exceptionalism

As the country mourns the awful killing of George Floyd in Minneapolis , we should reflect on the importance of human exceptionalism in fighting the poison of bigotry in all its forms .
Human exceptionalism understands that we each possess unique and immeasurable value simply and merely because we are human .
All of us possess equal intrinsic and inalienable moral value as an objective principle .
That is , we are not obligated to “ earn ” our status by possessing capabilities , attributes , or personal characteristics .
We are equal because we are human .
Nothing else @ @ @ @ @ @ @ perspective , racism is evil because it treats inherent equals as if they are unequal .
And in the fiction , the racist justifies discriminating against , denigrating , and/or objectifying the targeted victim based on the fallacy that “ the other ’ s ” life is somehow less important or worth valuing .
For people of faith , racism also rejects the victim as being equally made in the likeness and image of God .
Talk about hubris !
Racism also violates our duties to love and respect each other simply and @ @ @ @ @ @ @ behavior violates our duty as human beings to treat each other as we would want to be treated ourselves .
Bigotry is an unacceptable form of withholding .
It denies the mutual love we each owe to — and are entitled to receive from — all .
George Floyd is dead because the police officer who pinned his neck to the ground wanted to dominate his prisoner , perhaps because of Floyd ’ s race — or maybe , simply because the officer , drunk with his own power , simply thought he @ @ @ @ @ @ @ engaged to prevent danger or thwart harmful actions — is a gross abuse of authority .
It denies the respect we are each owed , and in a non-proper policing context , impedes human freedom .
The highest and best historical examples of constructive moral leadership have always emanated from a human exceptionalism core , sometimes radically so .
Jesus Christ ’ s Parable of the Good Samaritan stands not just for the proposition that we are to love our neighbor as we love ourselves , but also , that all are our @ @ @ @ @ @ @ in that example , the Samaritans — who had been traditionally scorned .
The ringing statement in the Declaration of Independence that all men are created equal , is steeped in human exceptionalism principles .
So too , Martin Luther King ’ s ringing pronouncement that we should all be judged by the content of our character rather than the color of our skin .
If we are ever to free ourselves of the scourge of racism that has caused so much suffering in our history , love that exhibits a deep regard @ @ @ @ @ @ @ simply and merely because we are all human — is the only way .
In this sense , human exceptionalism is the essential foundation of freedom and thriving , the only path to our society ’ s salvation .

Center on Human Exceptionalism
The mission of Discovery Institute’s Center on Human Exceptionalism is to affirm and uphold the intrinsic nature of human dignity, liberty, and equality. In resistance to a growing movement against unique human personhood, we aim to revitalize a commitment to the traditional Western view of human rights and human responsibilities — summed up by the term “human exceptionalism.” Read more


discoveryinstitute--2020-06-09--AI: Think About Ethics Before Trouble Arises

8 He has shown you , O mortal , what is good .
And what does the Lord require of you ?
To act justly and to love mercy and to walk humbly with your God .
( Micah 6:8 , NIV ) “ Why did the pastor cross the road ?
Because he wanted to get his chicken back.
” Don ’ t worry if you didn ’ t get the joke .
You ’ re not missing anything .
It is just not that funny .
This uncomfortably bad joke was an attempt at improving an even worse @ @ @ @ @ @ @ , which came up with the original , less intelligible , version : “ What do you call a pastor cross the road ?
He take the chicken.
” Yeah .
Deep-learning networks and AI systems have begun automating jobs across several industries , but stand-up comedy , at least , will be safe for a while .
Jokes aside , many ethical questions surround the development and use of artificial intelligence and machine learning systems .
In the popular media , visions of an impending robot takeover often raise concerns over how @ @ @ @ @ @ @ the future of humanity .
Some wonder whether we should require systems to be embedded with the equivalent of Asimov ’ s Laws of Robotics : • A robot may not injure a human being or , through inaction , allow a human being to come to harm .
• A robot must obey orders given to it by human beings except where such orders would conflict with the First Law .
• A robot must protect its own existence as long as such protection does not conflict with the First or Second @ @ @ @ @ @ @ when superhuman artificial intelligence will render us redundant , or possibly eliminate us entirely .
Such concerns are not entirely irrational , but they are a bit premature .
Our current systems are not consciousness in any way , nor is there a clear path for how to proceed towards that goal .
Simply adding more neurons to a deep-learning network isn ’ t likely to get us there .
It remains to be seen whether we can make approximations to minds good enough that they eventually behave like free and conscious beings @ @ @ @ @ @ @ have in place today are not sentient , but they are still dangerous .
I am not worried about the future of AI , but I am concerned about the dangers artificial learning systems currently pose .
There are obvious threats : weaponization , terrorism , fraud .
But there are also less intentional threats , such as increased inequality , privacy violations , and negligence resulting in harm .
For example , consider the case of the self-driving Uber vehicle that killed an Arizona pedestrian in March .
According to some , @ @ @ @ @ @ @ just isn ’ t ready yet , and releasing these cars on roads in their current form is an act of extreme negligence .
Cathy O ’ Neil , in her book , Weapons of Math Destruction highlights several cases of machine learning systems harming poor and minority groups through the uncritical use of questionable correlations in data .
For example , models that attempt to predict the likelihood that someone will commit a crime if released from prison ( criminal recidivism models ) , use early run-ins with police as a feature @ @ @ @ @ @ @ heavily policed and minority youth are prosecuted more often than their rich white counterparts for the same crimes , such as recreational drug use and possession , this feature of prediction strongly correlates with race and poverty levels , punishing those with the wrong economic background or skin color .
Another feature of these systems assesses whether a person lives near others who themselves have had trouble with the police .
While this feature may , in fact , correlate with a higher risk of committing a crime in the future , it @ @ @ @ @ @ @ done , which they have no control over .
Given that poorer people can not just move into wealthier neighborhoods to improve their statistics , the poor are doubly punished , simply for being poor .
Other examples are not hard to find .
Because of these dangers , ethics has become an area of increasing concern for AI researchers .
In light of these injustices , current mainstream interest in AI ethics is not surprising .
What is surprising , however , is that many of those who call for stronger ethical @ @ @ @ @ @ @ out against injustice , also believe there are no objective ethical standards .
They hold that all ethics and morals are merely subjective , personal value statements , no more significant than whether or not I like pineapple ice cream .
( I do not . )
Under this view , all ethics are merely social constructs , bound to particular times and cultures , with no transcendent aspect that grounds them in anything firmer than temporary human biases .
So if ethics are nothing but subjective personal biases , why would I @ @ @ @ @ @ @ I build ?
The first answer is that ethics are not merely subjective ; there are objective ethical standards that transcend time and culture , allowing entire cultures themselves to be condemned , such as the just condemnation of the Nazi culture of death and , in our own time , condemnation of the ISIS culture of slavery and violence .
If ethics did not transcend cultures , they could not judge between cultures , but we know that they do , and should .
Ethics rightly judges between cultures of oppression and @ @ @ @ @ @ @ The second answer to the question of why we ’ d want to include ethics in our learning systems is much more pragmatic : All learning systems need biases and assumptions to function well , so we lose nothing by including biases that are ethically just .
The Need for Bias in Learning By the 1980s , we knew that systems which were unbiased — meaning they did not make any assumptions beyond what was strictly consistent with training data — were systems that could not learn : they could memorize , but @ @ @ @ @ @ @ was presented with a new case , one that it hadn ’ t seen during training , it had no basis for choosing among the competing possibilities for dealing with the new example .
The existing training data was of no use because the system was not allowed to make any assumptions about how the observed training data was related to unseen test examples .
Such systems performed the equivalent of electronic coin flips to handle novel examples .
As a result , the distribution over the number of errors they made was @ @ @ @ @ @ @ times you ’ d be able to correctly predict the outcomes of a set of fair coin flips .
Biases are always necessary , but they are not always just or merciful .
As humans who strive to be just and merciful , we should make these biases reflect our highest ethical ideals , not our worst prejudices .
Although we would all agree on the importance of ethics in AI , there remains one lingering question : which ethics ?
There are as many ethical systems as there are different individuals and @ @ @ @ @ @ @ ethical framework we should operationalize within our learning systems .
Although objective ethics exist , we are not always guaranteed to know what they are and we often resist them once they become known—especially if they condemn our own behavior .
As a Christian who follows Jesus , I submit to an ethical framework which values human life , tries to protect the poor and vulnerable , and honors integrity .
In contrast , an agnostic may have an ethical orientation that values courage , love , and freedom .
Deciding how to @ @ @ @ @ @ @ is a hard problem which we will not solve here .
For the sake of our talk , we ’ ll simply assume that , through some process , a single ethical framework has been identified which we want to incorporate in our AI systems .
How do we do this ?
How can we operationalize a set of ethics in software ?
This seems like another hard problem .
But it is a problem we must solve in pursuit of justice .
So let ’ s work through one particular example of @ @ @ @ @ @ @ particular set of ethical principles .
While we don ’ t yet have a formula for compiling principles of ethics directly into code , we can identify places where free choices are made and show how ethical principles can guide those choices .
For concreteness , I will use my own ethical framework , specifically a set of three principles drawn from the Hebrew Bible , namely Micah 6:8 , which asks us what is required of man .
The answer given is that we are to act justly , love mercy , @ @ @ @ @ @ @ justly ; loving mercy ; walking humbly — I hope to persuade you that these are ethical standards that could be adopted by persons of any creed , despite their origin in the Judeo-Christian tradition .
They will serve as our test case for embedding ethical principles in systems .
As a developer of machine learning systems , formerly at Microsoft and now as an academic researcher , I actively shape my systems through the architecture choices I make and the biases , both conscious and unconscious , that I place within them @ @ @ @ @ @ @ but as we saw , biases are needed to allow for generalized learning , and I would add , are necessary for just operation in the real world .
We would like our learning systems to be just .
As reflections of us , people who act justly , we should create AI systems that also behave justly .
How can a system behave justly ?
By removing systematic prejudices that punish some through no fault of their own .
We ’ ve already seen an example of this , where law enforcement @ @ @ @ @ @ @ to better neighborhoods .
As a result , they receive worse treatment than those living in prosperous areas , even when convicted of similar crimes .
We should be vigilant .
To do so , we must question the assumptions built into our systems , and never stop questioning them .
Revisit them , explain them to others and provide justification for them that go beyond system efficiency or accuracy .
If you can not do so , then reject them .
Unjust prejudices do not become just simply because they boost the @ @ @ @ @ @ @ by independent parties often helps because we all have blind spots that prevent us from fully seeing our own biases in an objective light .
Interpretable machine learning systems such as decision trees and generalized additive models should be preferred over inscrutable black boxes like deep learning networks , wherever issues of justice are concerned .
If someone can not be told why a model condemns them , then they can not prove their own innocence , nor can we be certain that such condemnation is not the result of an inbuilt prejudice @ @ @ @ @ @ @ this regard , and we must continually fight against this trend .
Sloth is its own form of injustice .
When lives are affected by our algorithms , slackness is not an option , nor is apathy .
We ’ re told that one who is lazy in his work is the brother to him who destroys .
When a bridge collapses or an autonomous vehicle kills a pedestrian because of poorly tested software , we see this truth written in blood .
Acting justly prohibits short-cuts in the development process .
Developers @ @ @ @ @ @ @ no excuse for negligence .
So developers , write your unit tests .
Document your code .
Do code reviews , even though they are unpleasant .
Injustice is even more unpleasant .
Truth and justice are twin pillars , supporting and reinforcing one another .
What is just can only be established through what is true .
False witnesses in a court of law provide an obvious example of lies perverting justice .
In pursuit of justice , our systems must be built on truth .
This means paying more than lip-service @ @ @ @ @ @ @ imply causation .
Although A and B are always found together , this does not imply that A causes B ; on the contrary , B might cause A , or some other process C might be the cause of both A and B .
By pragmatic necessity , most statistical learning models are mere correlation models .
Correlation is easy to discover , whereas uncovering causation takes much more deliberate effort and experimentation .
When algorithms have real-world consequences it is important to keep this distinction in mind .
We can not @ @ @ @ @ @ @ which may lead to misguided intervention .
Machine learning researcher Rich Caruana has told the story of an AI system built for a healthcare client , which sought to model the probability of death for patients suffering from pneumonia .
Surprisingly , the system discovered that those who had previously been diagnosed with asthma had a lower probability of death : a correlation .
Those treating correlation as causation might enact a policy of inducing asthma to reduce the risk of death by pneumonia .
Such a policy would be disastrous , inflicting @ @ @ @ @ @ @ to this hasty conclusion : those with asthma were more likely to pay attention to changes in their breathing , uncovering early signs of pneumonia , leading to better outcomes .
Respecting the difference between correlation and causation led Rich and his collaborators to investigate the counter-intuitive link and discover the truth , rather than blindly trusting the output of the model .
A more sobering example comes from Bayes ’ theorem , a simple , widely-used , mathematical formula used for calculating conditional probabilities , as applied to proposed counter-terrorism efforts .
@ @ @ @ @ @ @ a particular religion .
Furthermore , assume that , among the general American population , only 1 % practice that religion and fewer than one in every hundred thousand Americans are terrorists .
If you encounter someone who practices “ the religion of terrorists , ” how likely is that person to actually be a terrorist ?
Bayes ’ theorem gives us a counterintuitive answer : it is not very likely .
The probability is less than 0.1 % , meaning that there is over 99.9 % probability that the person who practices @ @ @ @ @ @ @ .
Therefore , any policy that uses religion as the sole criterion for punishment will wrongly condemn 999 out of every 1000 punished .
AI systems can be fooled in the exact same way .
Whenever learning systems , even highly accurate systems , try to detect rare events , Bayes ’ theorem cautions us that the outcomes detected can overwhelmingly be false positives .
Questioning our models and seeking to independently verify the truth of their outputs can save us from committing injustice .
We ’ ve seen that a just learning @ @ @ @ @ @ @ interpretable , whose internal biases and assumptions are questioned often and independently justified , whose outputs are verified , and whose behavior is thoroughly tested .
The results from these systems are still not blindly trusted as truth itself but are further investigated , bringing to bear our best mathematical and statistical knowledge .
Just models must be continually purified of unjust biases , untrue premises , and unjustified gaps in logic .
In addition to justice , let us also have mercy .
Mercy is refraining from doing what is within our @ @ @ @ @ @ @ .
It is similar to the Christian concept of grace , which is unmerited favor .
While mercy withholds a punishment that is rightly deserved , grace foregoes punishment and instead gives a gift which is not deserved .
Mercy is a choice , often a difficult choice , to refrain from seeking revenge or retribution .
It is a choice to refrain from exterminating an enemy , but to seek to redeem him .
It is embodied in the meekness that refuses to use a possessed advantage , not because it would @ @ @ @ @ @ @ be unkind .
As learning systems grow in power , amassing advantages in speed and reach , it is crucial that they embody the virtue of mercy within them .
Choosing to not exploit the easily exploitable can be viewed as a form of mercy because those who are driven purely by economics will seek any advantage that produces a profit , with the weak and uneducated suffering the greatest disadvantage .
For example , some insurance companies employ learning models to predict how likely someone is to shop around for insurance and @ @ @ @ @ @ @ is unlikely to do so .
What happens when the bulk of those affected are poor and less-educated , including recent immigrants who are less likely to realize they ’ re being ripped off ?
They ’ re easy targets .
Should our systems exploit their weaknesses or should we extend mercy ?
Whenever we spot potential advantages , mercy should motivate us to stop and weigh carefully what the consequences of using our advantage will be .
Just because we can do something , does not mean we should .
As someone @ @ @ @ @ @ @ punch you in the nose does not imply that I should punch you in the nose .
Mercy keeps us from punching others even when they deserve it .
And how much more so when they don ’ t .
To love mercy sometimes means to give up efficiency .
It could mean losing a few points of model accuracy by refusing to take into account features that invade privacy or are proxies for race , leading to discriminatory model behavior .
But that ’ s OK .
The merciful are willing to @ @ @ @ @ @ @ advantages so they can help others .
Mercy extends to producing beauty instead of baseness .
As AI developers , we can choose how we apply our gifts : in ways that promote human flourishing , or in ways that produce gain at the expense of others .
We can choose to build products that increase depression and encourage addiction , or we can focus on systems that help the poor , the lost , and the vulnerable .
The latter may not generate as much wealth , but mercy teaches us the @ @ @ @ @ @ @ gain for the sake of serving others .
As a Christian , I see the mercy extended to us by a suffering Jesus as the most beautiful and noble in all history , voluntarily setting aside the glory that was rightfully his in order to take on the form of a servant .
Where mercy flows , beauty abounds .
Furthermore , we are not told to simply be merciful , but to love mercy .
We should seek out opportunities to be merciful , and not give mercy begrudgingly .
Greater power @ @ @ @ @ @ @ , let us consider the goodness of walking humbly .
Humility is knowing your “ right size ” in this world , knowing yourself and your limits .
This knowledge relates to our learning systems in three ways .
First , because our limitations cause bugs , errors , and unfounded assumptions to slip into our work , humility requires that we constantly verify what we ’ ve done and seek to evaluate it against objective measures .
Humble people will rightly recognize their own tendency to err and seek external accountability and @ @ @ @ @ @ @ learning systems as expressions of developer bias , or as one author put it , “ opinions embedded in mathematics.
” Thus , we will be skeptical of our own models and the models of others .
We will not suffer the illusion of unbiased objectivity in our models but will examine the biases within them , making them as explicit and open to investigation as possible .
Third , we will recognize the limitations of data science itself and the pitfalls of working with incomplete data in a messy world .
Noisy @ @ @ @ @ @ @ overfitting are just some of the problems that plague learning systems .
Just as we question ourselves and our models , we should also question the data we use and the capabilities of mathematical predictive systems .
Humility reminds us that we , the systems we build , and the data we use , may all contain error .
It is our responsibility to stop simply propagating that error , but to uncover it , study it , and eliminate it if possible .
When not possible , then to set aside the @ @ @ @ @ @ @ To walk humbly is to question continually .
As truth undergirds freedom , and mercy gives rise to beauty , we see that humility is the good and proper relation of man to that which is not man .
Goodness itself is the right relationship between action and obligation , between object and place , between form and purpose .
Humility is simply recognizing our true nature and our rightful place , moving ourselves towards where we belong , in relationship , walking alongside the one who created us for that express purpose @ @ @ @ @ @ @ we have seen how ethics can radically inform goals and guide choices when developing learning systems .
For those with different ethical frameworks , I challenge you to consider how your particular ethical stance will inform your own system choices .
Ethics should be the foundation , not an afterthought .
Make the act of embedding ethics explicit , rather than passively allowing your systems to be shaped by mere expedience .
Root your perspective and decisions in principles that are more substantial , that bring forth beauty and justice .
The act @ @ @ @ @ @ @ given you practice .
May your systems reflect truth , beauty , and goodness , if not justice , mercy , and humility .
George D. Montañez is an assistant professor of computer science at Harvey Mudd College , in Claremont , California , specializing in machine learning .
Also by George Montañez : What is learning anyway ?

8 He has shown you, O mortal, what is good.
And what does the Lord require of you?
To act justly and to love mercy
and to walk humbly with your God. (Micah 6:8, NIV)
(This talk was given on 10/26/2018 at the 2018 Dale P. Jones Business Ethics Forum, at Baylor University in Waco, Texas. The theme of the forum was The Ethics of Artificial Intelligence.” – ed.)
“Why did the pastor cross the road?
Because he wanted to get his chicken back.”
Don’t worry if you didn’t get the joke. You’re not missing anything. It is just not that funny.
This uncomfortably bad joke was an attempt at improving an even worse joke generated by an artificial neural network, which came up with the original, less intelligible, version:
“What do you call a pastor cross the road?
He take the chicken.”
Yeah. Deep-learning networks and AI systems have begun automating jobs across several industries, but stand-up comedy, at least, will be safe for a while.
Fear of AI’s Future
Jokes aside, many ethical questions surround the development and use of artificial intelligence and machine learning systems. In the popular media, visions of an impending robot takeover often raise concerns over how we should shape AI systems to safeguard the future of humanity. Some wonder whether we should require systems to be embedded with the equivalent of Asimov’s Laws of Robotics:
A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey orders given to it by human beings except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
Others worry about the Singularity when superhuman artificial intelligence will render us redundant, or possibly eliminate us entirely. Such concerns are not entirely irrational, but they are a bit premature. Our current systems are not consciousness in any way, nor is there a clear path for how to proceed towards that goal. Simply adding more neurons to a deep-learning network isn’t likely to get us there. It remains to be seen whether we can make approximations to minds good enough that they eventually behave like free and conscious beings.
Existing Dangers of AI Systems
The AI and ML systems we have in place today are not sentient, but they are still dangerous. I am not worried about the future of AI, but I am concerned about the dangers artificial learning systems currently pose. There are obvious threats: weaponization, terrorism, fraud. But there are also less intentional threats, such as increased inequality, privacy violations, and negligence resulting in harm. For example, consider the case of the self-driving Uber vehicle that killed an Arizona pedestrian in March. According to some, that fatal crash proved that self-driving technology just isn’t ready yet, and releasing these cars on roads in their current form is an act of extreme negligence.
Cathy O’Neil, in her book, Weapons of Math Destruction highlights several cases of machine learning systems harming poor and minority groups through the uncritical use of questionable correlations in data. For example, models that attempt to predict the likelihood that someone will commit a crime if released from prison (criminal recidivism models), use early run-ins with police as a feature. Because poor minority neighborhoods are more heavily policed and minority youth are prosecuted more often than their rich white counterparts for the same crimes, such as recreational drug use and possession, this feature of prediction strongly correlates with race and poverty levels, punishing those with the wrong economic background or skin color. Another feature of these systems assesses whether a person lives near others who themselves have had trouble with the police. While this feature may, in fact, correlate with a higher risk of committing a crime in the future, it punishes the person for what others have done, which they have no control over. Given that poorer people cannot just move into wealthier neighborhoods to improve their statistics, the poor are doubly punished, simply for being poor. Other examples are not hard to find. Because of these dangers, ethics has become an area of increasing concern for AI researchers.
Why Ethics?
In light of these injustices, current mainstream interest in AI ethics is not surprising. What is surprising, however, is that many of those who call for stronger ethical safeguards, fighting against inequality and speaking out against injustice, also believe there are no objective ethical standards. They hold that all ethics and morals are merely subjective, personal value statements, no more significant than whether or not I like pineapple ice cream. (I do not.) Under this view, all ethics are merely social constructs, bound to particular times and cultures, with no transcendent aspect that grounds them in anything firmer than temporary human biases. So if ethics are nothing but subjective personal biases, why would I want to include them in the systems I build?
The first answer is that ethics are not merely subjective; there are objective ethical standards that transcend time and culture, allowing entire cultures themselves to be condemned, such as the just condemnation of the Nazi culture of death and ,in our own time, condemnation of the ISIS culture of slavery and violence. If ethics did not transcend cultures, they could not judge between cultures, but we know that they do, and should. Ethics rightly judges between cultures of oppression and their victims. Justice requires it. The second answer to the question of why we’d want to include ethics in our learning systems is much more pragmatic: All learning systems need biases and assumptions to function well, so we lose nothing by including biases that are ethically just.
The Need for Bias in Learning
By the 1980s, we knew that systems which were unbiased — meaning they did not make any assumptions beyond what was strictly consistent with training data — were systems that could not learn: they could memorize, but never generalize. When such a system was presented with a new case, one that it hadn’t seen during training, it had no basis for choosing among the competing possibilities for dealing with the new example. The existing training data was of no use because the system was not allowed to make any assumptions about how the observed training data was related to unseen test examples. Such systems performed the equivalent of electronic coin flips to handle novel examples. As a result, the distribution over the number of errors they made was exactly the distribution of the number of times you’d be able to correctly predict the outcomes of a set of fair coin flips. Biases are always necessary, but they are not always just or merciful. As humans who strive to be just and merciful, we should make these biases reflect our highest ethical ideals, not our worst prejudices.
Which Ethics?
Although we would all agree on the importance of ethics in AI, there remains one lingering question: which ethics? There are as many ethical systems as there are different individuals and it’s not immediately clear which ethical framework we should operationalize within our learning systems. Although objective ethics exist, we are not always guaranteed to know what they are and we often resist them once they become known—especially if they condemn our own behavior. As a Christian who follows Jesus, I submit to an ethical framework which values human life, tries to protect the poor and vulnerable, and honors integrity. In contrast, an agnostic may have an ethical orientation that values courage, love, and freedom. Deciding how to choose one particular ethical framework over another is a hard problem which we will not solve here. For the sake of our talk, we’ll simply assume that, through some process, a single ethical framework has been identified which we want to incorporate in our AI systems. How do we do this?
Operationalizing Ethics
How can we operationalize a set of ethics in software? This seems like another hard problem. But it is a problem we must solve in pursuit of justice. So let’s work through one particular example of what it would look like for a particular set of ethical principles. While we don’t yet have a formula for compiling principles of ethics directly into code, we can identify places where free choices are made and show how ethical principles can guide those choices. For concreteness, I will use my own ethical framework, specifically a set of three principles drawn from the Hebrew Bible, namely Micah 6:8, which asks us what is required of man. The answer given is that we are to act justly, love mercy, and walk humbly with God. Acting justly; loving mercy; walking humbly — I hope to persuade you that these are ethical standards that could be adopted by persons of any creed, despite their origin in the Judeo-Christian tradition. They will serve as our test case for embedding ethical principles in systems. As a developer of machine learning systems, formerly at Microsoft and now as an academic researcher, I actively shape my systems through the architecture choices I make and the biases, both conscious and unconscious, that I place within them. Not all biases are equal, but as we saw, biases are needed to allow for generalized learning, and I would add, are necessary for just operation in the real world.
Acting Justly
We would like our learning systems to be just. As reflections of us, people who act justly, we should create AI systems that also behave justly. How can a system behave justly? By removing systematic prejudices that punish some through no fault of their own. We’ve already seen an example of this, where law enforcement models punish those too poor to move to better neighborhoods. As a result, they receive worse treatment than those living in prosperous areas, even when convicted of similar crimes. We should be vigilant. To do so, we must question the assumptions built into our systems, and never stop questioning them. Revisit them, explain them to others and provide justification for them that go beyond system efficiency or accuracy. If you cannot do so, then reject them. Unjust prejudices do not become just simply because they boost the bottom line. External review of code by independent parties often helps because we all have blind spots that prevent us from fully seeing our own biases in an objective light. Interpretable machine learning systems such as decision trees and generalized additive models should be preferred over inscrutable black boxes like deep learning networks, wherever issues of justice are concerned. If someone cannot be told why a model condemns them, then they cannot prove their own innocence, nor can we be certain that such condemnation is not the result of an inbuilt prejudice. Many learning systems are problematic in this regard, and we must continually fight against this trend.
Sloth is its own form of injustice. When lives are affected by our algorithms, slackness is not an option, nor is apathy. We’re told that one who is lazy in his work is the brother to him who destroys. When a bridge collapses or an autonomous vehicle kills a pedestrian because of poorly tested software, we see this truth written in blood. Acting justly prohibits short-cuts in the development process. Developers can be lazy, but laziness is no excuse for negligence. So developers, write your unit tests. Document your code. Do code reviews, even though they are unpleasant. Injustice is even more unpleasant.
Truth and justice are twin pillars, supporting and reinforcing one another. What is just can only be established through what is true. False witnesses in a court of law provide an obvious example of lies perverting justice. In pursuit of justice, our systems must be built on truth. This means paying more than lip-service to the fact that correlation does not imply causation. Although A and B are always found together, this does not imply that A causes B; on the contrary, B might cause A, or some other process C might be the cause of both A and B. By pragmatic necessity, most statistical learning models are mere correlation models. Correlation is easy to discover, whereas uncovering causation takes much more deliberate effort and experimentation. When algorithms have real-world consequences it is important to keep this distinction in mind. We cannot afford to treat correlations as causation, which may lead to misguided intervention.
Machine learning researcher Rich Caruana has told the story of an AI system built for a healthcare client, which sought to model the probability of death for patients suffering from pneumonia. Surprisingly, the system discovered that those who had previously been diagnosed with asthma had a lower probability of death: a correlation. Those treating correlation as causation might enact a policy of inducing asthma to reduce the risk of death by pneumonia. Such a policy would be disastrous, inflicting unjust harm because the truth was opposite to this hasty conclusion: those with asthma were more likely to pay attention to changes in their breathing, uncovering early signs of pneumonia, leading to better outcomes. Respecting the difference between correlation and causation led Rich and his collaborators to investigate the counter-intuitive link and discover the truth, rather than blindly trusting the output of the model.
A more sobering example comes from Bayes’ theorem, a simple, widely-used, mathematical formula used for calculating conditional probabilities, as applied to proposed counter-terrorism efforts. Assume that 99% of terrorists practice a particular religion. Furthermore, assume that, among the general American population, only 1% practice that religion and fewer than one in every hundred thousand Americans are terrorists. If you encounter someone who practices “the religion of terrorists,” how likely is that person to actually be a terrorist? Bayes’ theorem gives us a counterintuitive answer: it is not very likely. The probability is less than 0.1%, meaning that there is over 99.9% probability that the person who practices that religion is not a terrorist,. Therefore, any policy that uses religion as the sole criterion for punishment will wrongly condemn 999 out of every 1000 punished.
AI systems can be fooled in the exact same way. Whenever learning systems, even highly accurate systems, try to detect rare events, Bayes’ theorem cautions us that the outcomes detected can overwhelmingly be false positives. Questioning our models and seeking to independently verify the truth of their outputs can save us from committing injustice.
We’ve seen that a just learning system is one whose internal model is interpretable, whose internal biases and assumptions are questioned often and independently justified, whose outputs are verified, and whose behavior is thoroughly tested. The results from these systems are still not blindly trusted as truth itself but are further investigated, bringing to bear our best mathematical and statistical knowledge. Just models must be continually purified of unjust biases, untrue premises, and unjustified gaps in logic.
Loving Mercy
In addition to justice, let us also have mercy. Mercy is refraining from doing what is within our power to do, because of compassion. It is similar to the Christian concept of grace, which is unmerited favor. While mercy withholds a punishment that is rightly deserved, grace foregoes punishment and instead gives a gift which is not deserved. Mercy is a choice, often a difficult choice, to refrain from seeking revenge or retribution. It is a choice to refrain from exterminating an enemy, but to seek to redeem him. It is embodied in the meekness that refuses to use a possessed advantage, not because it would be unfair, but because it would be unkind. As learning systems grow in power, amassing advantages in speed and reach, it is crucial that they embody the virtue of mercy within them.
Choosing to not exploit the easily exploitable can be viewed as a form of mercy because those who are driven purely by economics will seek any advantage that produces a profit, with the weak and uneducated suffering the greatest disadvantage. For example, some insurance companies employ learning models to predict how likely someone is to shop around for insurance and charge more when they think the person is unlikely to do so. What happens when the bulk of those affected are poor and less-educated, including recent immigrants who are less likely to realize they’re being ripped off? They’re easy targets. Should our systems exploit their weaknesses or should we extend mercy?
‘Are there no prisons.’ said the Spirit, turning on [Ebenezer Scrooge] for the last time with his own words. ‘Are there no workhouses?’/John Leech, 1843, courtesy Dan Calinescu
Whenever we spot potential advantages, mercy should motivate us to stop and weigh carefully what the consequences of using our advantage will be. Just because we can do something, does not mean we should. As someone once wrote, just because I can punch you in the nose does not imply that I should punch you in the nose. Mercy keeps us from punching others even when they deserve it. And how much more so when they don’t.
To love mercy sometimes means to give up efficiency. It could mean losing a few points of model accuracy by refusing to take into account features that invade privacy or are proxies for race, leading to discriminatory model behavior. But that’s OK. The merciful are willing to give up some of their rights and advantages so they can help others.
Mercy extends to producing beauty instead of baseness. As AI developers, we can choose how we apply our gifts: in ways that promote human flourishing, or in ways that produce gain at the expense of others. We can choose to build products that increase depression and encourage addiction, or we can focus on systems that help the poor, the lost, and the vulnerable. The latter may not generate as much wealth, but mercy teaches us the nobility of giving up what we might gain for the sake of serving others. As a Christian, I see the mercy extended to us by a suffering Jesus as the most beautiful and noble in all history, voluntarily setting aside the glory that was rightfully his in order to take on the form of a servant. Where mercy flows, beauty abounds.
Furthermore, we are not told to simply be merciful, but to love mercy. We should seek out opportunities to be merciful, and not give mercy begrudgingly. Greater power should inspire ever greater mercy.
Walking Humbly
Lastly, let us consider the goodness of walking humbly. Humility is knowing your “right size” in this world, knowing yourself and your limits. This knowledge relates to our learning systems in three ways. First, because our limitations cause bugs, errors, and unfounded assumptions to slip into our work, humility requires that we constantly verify what we’ve done and seek to evaluate it against objective measures. Humble people will rightly recognize their own tendency to err and seek external accountability and review. Second, we will recognize learning systems as expressions of developer bias, or as one author put it, “opinions embedded in mathematics.” Thus, we will be skeptical of our own models and the models of others. We will not suffer the illusion of unbiased objectivity in our models but will examine the biases within them, making them as explicit and open to investigation as possible. Third, we will recognize the limitations of data science itself and the pitfalls of working with incomplete data in a messy world. Noisy data, missing information, and model overfitting are just some of the problems that plague learning systems. Just as we question ourselves and our models, we should also question the data we use and the capabilities of mathematical predictive systems. Humility reminds us that we, the systems we build, and the data we use, may all contain error. It is our responsibility to stop simply propagating that error, but to uncover it, study it, and eliminate it if possible. When not possible, then to set aside the systems until it can be eliminated. To walk humbly is to question continually.
As truth undergirds freedom, and mercy gives rise to beauty, we see that humility is the good and proper relation of man to that which is not man. Goodness itself is the right relationship between action and obligation, between object and place, between form and purpose. Humility is simply recognizing our true nature and our rightful place, moving ourselves towards where we belong, in relationship, walking alongside the one who created us for that express purpose.
Ethics as the Foundation
Beginning with simple ethical principles, we have seen how ethics can radically inform goals and guide choices when developing learning systems. For those with different ethical frameworks, I challenge you to consider how your particular ethical stance will inform your own system choices. Ethics should be the foundation, not an afterthought. Make the act of embedding ethics explicit, rather than passively allowing your systems to be shaped by mere expedience. Root your perspective and decisions in principles that are more substantial, that bring forth beauty and justice. The act of working through this together has now given you practice. May your systems reflect truth, beauty, and goodness, if not justice, mercy, and humility.
Also by George Montañez: What is learning anyway?


discoveryinstitute--2020-06-07--Twenty Years On, Aliens Still Cause Global Warming

In 2003 , author and filmmaker Michael Crichton ( 1942–2008 ) , best known for Jurassic Park , made a now-famous speech at Caltech , titled “ Aliens Cause Global Warming.
” The title was humorous but the content was serious .
He was not addressing some strange theory of global warming ; he was warning about the politicization of science .
Crichton ( left , in 2002 , courtesy Jon Chase , Harvard CC 3.0 ) , noted that , over the years , science has drifted away from its foundation as an objective search for truth and given @ @ @ @ @ @ @ The first time that he witnessed that was with the famous Drake Equation , used to turn SETI speculations about space aliens into a science .
The Drake equation was a series of probabilities multiplied together to estimate the probability that space aliens may exist who can communicate with us .
Of course , none of the terms is known or even estimable , and they may not be expressible as probabilities .
However , SETI was given a pass because it suited the scientific zeitgeist of the day .
It probably helped @ @ @ @ @ @ @ same thing happened during discussions of the effects of nuclear war .
Paper after paper made nonsense claims about such a war ’ s effects , including nuclear winter .
But no one wanted to intervene , fearing that skepticism might be portrayed as a defense of nuclear war .
Thus , bad science , even from top-tier journals , was reported as fact by the scientific community .
Crichton noted that some of these papers were actually part of an orchestrated media campaign : He summed it all up by saying , @ @ @ @ @ @ @ is done , it is the way products are sold.
” Painful , but true .
Unfortunately , these problems in science remain and affect coverage of the COVID-19 crisis today .
I ’ ll give two examples , both from The Lancet , which is one of the world ’ s oldest and most respected peer-reviewed medical journals .
The first one is “ Mortality Before and After the 2003 Invasion of Iraq : Cluster Sample Survey ” This meaningless study was rushed to publication in time for the 2004 election , @ @ @ @ @ @ @ policy .
That fact alone should set off warning bells about the content .
But the paper itself is worse .
The paper claims ( quite emphatically , and several times ) that the civilian body count for the Iraq war was around 100,000 .
And that was the number reported in all significant media outlets .
But , if you looked at the details , you would find that the actual range was between 8,000 and 194,000 casualties .
With so wide a range of estimates , it was basically a worthless @ @ @ @ @ @ @ publication in one of the most prestigious medical journals in order to influence an election .
As Crichton said , “ This is not the way science is done , it is the way products are sold.
” Now , in the midst of the current COVID-19 crisis , another questionable study has been released , again by The Lancet .
This study claimed that , not only was hydroxychloroquine not helpful in fighting COVID-19 , it was actually dangerous .
The World Health Organization ( WHO ) cancelled its trials of the @ @ @ @ @ @ @ problem ?
The data is totally bogus .
The now retracted study overstated COVID cases and deaths , sometimes by a factor of 80 .
Why the rush to publish a study with such bad data ?
Is it because an unpopular public figure was touting hydroxychloroquine , which made everyone look the other way when data that appeared to refute him didn ’ t match up ?
What is even more disturbing is that influential groups are using such studies to form the basis for censorship—not just of controversialists—but even of world @ @ @ @ @ @ @ possibly on the basis of this very study , removed the public statements of Brazilian president Jair Bolsonaro which claimed that hydroxychloroquine was working .
Why ?
Because it breached their “ misinformation guidelines.
” That is , the post contained information which was at odds with established science—and the established science in this case is a now-retracted study .
So , what we have is politically motivated science that is backed up by scientifically motivated censorship .
The science pretends to be an unbiased source of truth and the big media companies @ @ @ @ @ @ @ removing content of which they disapprove—all the while appearing high-minded .
It ’ s traditional political censorship dressed up to look like concern for “ science.
” In short , the worry that science might be co-opted by non-scientific motivations is not merely theoretical .
The Lancet peer review scandal around hydroxychloroquine and COVID-19 shows that science has already been co-opted by political interests : From now on , we must all be prepared to treat skeptically claims that are “ vetted in top tier peer-reviewed journals.
” Further reading : Why it @ @ @ @ @ @ @ review .
Robert J .
Marks : Reformers are battling numerical laws that govern how incentives work .
Know your enemy !

In 2003, author and filmmaker Michael Crichton (1942–2008), best known for Jurassic Park, made a now-famous speech at Caltech, titled “Aliens Cause Global Warming.” The title was humorous but the content was serious. He was not addressing some strange theory of global warming; he was warning about the politicization of science.
Crichton (left, in 2002, courtesy Jon Chase, Harvard CC 3.0), noted that, over the years, science has drifted away from its foundation as an objective search for truth and given itself over to political power games. The first time that he witnessed that was with the famous Drake Equation, used to turn SETI speculations about space aliens into a science. The Drake equation was a series of probabilities multiplied together to estimate the probability that space aliens may exist who can communicate with us. Of course, none of the terms is known or even estimable, and they may not be expressible as probabilities. However, SETI was given a pass because it suited the scientific zeitgeist of the day. It probably helped capture public attention for science.
The same thing happened during discussions of the effects of nuclear war. Paper after paper made nonsense claims about such a war’s effects, including nuclear winter. But no one wanted to intervene, fearing that skepticism might be portrayed as a defense of nuclear war. Thus, bad science, even from top-tier journals, was reported as fact by the scientific community.
Crichton noted that some of these papers were actually part of an orchestrated media campaign:
The first announcement of nuclear winter appeared in an article by Sagan in the Sunday supplement, Parade. The very next day, a highly-publicized, high-profile conference on the long-term consequences of nuclear war was held in Washington, chaired by Carl Sagan and Paul Ehrlich, the most famous and media-savvy scientists of their generation. Sagan appeared on the Johnny Carson show 40 times. Ehrlich was on 25 times. Following the conference, there were press conferences, meetings with congressmen, and so on. The formal papers in Science came months later. Michael Crichton, “Aliens Cause Global Warming” at California Institute of Technology, Pasadena, CA (January 17, 2003)
He summed it all up by saying, “This is not the way science is done, it is the way products are sold.” Painful, but true.
Unfortunately, these problems in science remain and affect coverage of the COVID-19 crisis today. I’ll give two examples, both from The Lancet, which is one of the world’s oldest and most respected peer-reviewed medical journals.
The first one is “Mortality Before and After the 2003 Invasion of Iraq: Cluster Sample Survey” This meaningless study was rushed to publication in time for the 2004 election, precisely because it was meant to influence policy. That fact alone should set off warning bells about the content.
But the paper itself is worse. The paper claims (quite emphatically, and several times) that the civilian body count for the Iraq war was around 100,000. And that was the number reported in all significant media outlets. But, if you looked at the details, you would find that the actual range was between 8,000 and 194,000 casualties. With so wide a range of estimates, it was basically a worthless study. Yet it was rushed to publication in one of the most prestigious medical journals in order to influence an election. As Crichton said, “This is not the way science is done, it is the way products are sold.”
Now, in the midst of the current COVID-19 crisis, another questionable study has been released, again by The Lancet. This study claimed that, not only was hydroxychloroquine not helpful in fighting COVID-19, it was actually dangerous. The World Health Organization (WHO) cancelled its trials of the drug due to this study.
The problem? The data is totally bogus. The now retracted study overstated COVID cases and deaths, sometimes by a factor of 80. Why the rush to publish a study with such bad data? Is it because an unpopular public figure was touting hydroxychloroquine, which made everyone look the other way when data that appeared to refute him didn’t match up?
What is even more disturbing is that influential groups are using such studies to form the basis for censorship—not just of controversialists—but even of world leaders. Facebook, for instance, possibly on the basis of this very study, removed the public statements of Brazilian president Jair Bolsonaro which claimed that hydroxychloroquine was working. Why? Because it breached their “misinformation guidelines.” That is, the post contained information which was at odds with established science—and the established science in this case is a now-retracted study.
So, what we have is politically motivated science that is backed up by scientifically motivated censorship. The science pretends to be an unbiased source of truth and the big media companies use such claims as a pretense for removing content of which they disapprove—all the while appearing high-minded. It’s traditional political censorship dressed up to look like concern for “science.”
In short, the worry that science might be co-opted by non-scientific motivations is not merely theoretical. The Lancet peer review scandal around hydroxychloroquine and COVID-19 shows that science has already been co-opted by political interests: From now on, we must all be prepared to treat skeptically claims that are “vetted in top tier peer-reviewed journals.”
Further reading: Why it’s so hard to reform peer review. Robert J. Marks: Reformers are battling numerical laws that govern how incentives work. Know your enemy!


discoveryinstitute--2020-06-04--Racism, Evolution, and Human Exceptionalism

Some , like our friend and colleague Michael Medved , have seen a complex “ design ” at work in history .
Certainly , the knotting in the present situation in the United States is so complex — the pandemic , the lockdown , the economic catastrophe , the death of George Floyd , the protests , the riots and looting , now the curfews , what next ?
— as to be nearly impossible to untangle .
Certain threads in the events of recent months suggest the hand of a skilled novelist .
Think of the role played by @ @ @ @ @ @ @ We ’ ve seen a virus that terrifies because it robs victims of breath , masks to protect us from the virus that themselves impede breathing , a lockdown that suffocates enterprise even as it brings fresh breathable air to cities because people stop driving their cars , a brutal death in which the victim pleads , “ I can ’ t breathe , ” pepper spray to disperse unruly protests that , again , works because it makes it takes one ’ s breath away .
The tapestry of the moment extends @ @ @ @ @ @ @ in the Wall Street Journal on Tuesday reported the death of a woman who was the last recipient of a veteran ’ s pension from the Union Army .
The veteran was her father .
The reminder that the Civil War was not so long ago struck me , too , as a storyteller ’ s touch .
Slavery , racism , sectional division — these forces that drove America ’ s greatest conflict remain powerful and raw .
Who Can Analyze It All ?
From this tangle of motifs , Wesley Smith @ @ @ @ @ @ @ one lesson relevant to our mission here : that the exceptional place of man in nature is a rebuke to racism .
From “ Racism Violates Human Exceptionalism , ” at the Humanize blog : From a human exceptionalist perspective , racism is evil because it treats inherent equals as if they are unequal .
And in the fiction , the racist justifies discriminating against , denigrating , and/or objectifying the targeted victim based on the fallacy that “ the other ’ s ” life is somehow less important or worth valuing .
@ @ @ @ @ @ @ rejects the victim as being equally made in the likeness and image of God .
Talk about hubris !
Racism also violates our duties to love and respect each other simply and merely because we are human .
Discriminatory behavior violates our duty as human beings to treat each other as we would want to be treated ourselves .
Bigotry is an unacceptable form of withholding .
It denies the mutual love we each owe to — and are entitled to receive from — all .
George Floyd is dead because the police @ @ @ @ @ @ @ ground wanted to dominate his prisoner , perhaps because of Floyd ’ s race — or maybe , simply because the officer , drunk with his own power , simply thought he could .
Domination — when not temporarily engaged to prevent danger or thwart harmful actions — is a gross abuse of authority .
It denies the respect we are each owed , and in a non-proper policing context , impedes human freedom .
The Themes of the Hour Discrimination versus dignity , respect versus domination , freedom and creativity versus suffocation @ @ @ @ @ @ @ — these are all the motifs of the hour .
And looked at from a height , they are the themes of Discovery Institute ’ s programs .
Recognizing the intelligent design in life is an antidote to the most negative ideas circulating in our culture .
By contrast , seeing human beings as natural flotsam , tossed up by a random , unplanned material process without purpose or meaning , can contribute nothing positive .
It only degrades and impoverishes .
The scientific debate about evolution can be set alongside the cultural @ @ @ @ @ @ @ Discovery Institute documentaries like Human Zoos and The Biology of the Second Reich .
A direct line connects Charles Darwin himself with these downstream effects .
In The Descent of Man , Darwin emphasized that humans are only animals in the end : “ Man is constructed on the same general type or model with other mammals.
” “ There is no fundamental difference between man and the higher mammals in their mental faculties.
” There is nothing special about our origins : “ I have at least , ” he wrote , @ @ @ @ @ @ @ service in aiding to overthrow the dogma of separate creations.
” From his observations , Darwin drew the conclusion of a racial hierarchy with Africans at the bottom .
He predicted that “ The civilized races of man will almost certainly exterminate and replace the savage races through the world.
” Darwinian racism , with its denial of mankind ’ s exceptional status , flowed like a river into the textbooks that American high school biology students learned from for decades .
Only the civil rights movement began to turn that river backward @ @ @ @ @ @ @ influence of scientific materialism on scientific racism and , as Discovery Institute ’ s John West has noted here , he “ spoke forcefully against the idea that humans are the products of a blind material process.
” The design in U.S. history , which we ’ re living through right now , can ’ t be understood without due regard for this crucial background .

Some, like our friend and colleague Michael Medved, have seen a complex “design” at work in history. Certainly, the knotting in the present situation in the United States is so complex — the pandemic, the lockdown, the economic catastrophe, the death of George Floyd, the protests, the riots and looting, now the curfews, what next? — as to be nearly impossible to untangle.
Certain threads in the events of recent months suggest the hand of a skilled novelist. Think of the role played by an elementary physiological process, breathing. We’ve seen a virus that terrifies because it robs victims of breath, masks to protect us from the virus that themselves impede breathing, a lockdown that suffocates enterprise even as it brings fresh breathable air to cities because people stop driving their cars, a brutal death in which the victim pleads, “I can’t breathe,” pepper spray to disperse unruly protests that, again, works because it makes it takes one’s breath away.
The tapestry of the moment extends further back in time. An article in the Wall Street Journal on Tuesday reported the death of a woman who was the last recipient of a veteran’s pension from the Union Army. The veteran was her father. The reminder that the Civil War was not so long ago struck me, too, as a storyteller’s touch. Slavery, racism, sectional division — these forces that drove America’s greatest conflict remain powerful and raw.
Who Can Analyze It All?
From this tangle of motifs, Wesley Smith of the Center on Human Exceptionalism extracts one lesson relevant to our mission here: that the exceptional place of man in nature is a rebuke to racism.
From “Racism Violates Human Exceptionalism,” at the Humanize blog:
From a human exceptionalist perspective, racism is evil because it treats inherent equals as if they are unequal. And in the fiction, the racist justifies discriminating against, denigrating, and/or objectifying the targeted victim based on the fallacy that “the other’s” life is somehow less important or worth valuing. For people of faith, racism also rejects the victim as being equally made in the likeness and image of God. Talk about hubris! Racism also violates our duties to love and respect each other simply and merely because we are human. Discriminatory behavior violates our duty as human beings to treat each other as we would want to be treated ourselves. Bigotry is an unacceptable form of withholding. It denies the mutual love we each owe to — and are entitled to receive from — all. George Floyd is dead because the police officer who pinned his neck to the ground wanted to dominate his prisoner, perhaps because of Floyd’s race — or maybe, simply because the officer, drunk with his own power, simply thought he could. Domination — when not temporarily engaged to prevent danger or thwart harmful actions — is a gross abuse of authority. It denies the respect we are each owed, and in a non-proper policing context, impedes human freedom.
The Themes of the Hour
Discrimination versus dignity, respect versus domination, freedom and creativity versus suffocation, ordered liberty versus the human zoo — these are all the motifs of the hour. And looked at from a height, they are the themes of Discovery Institute’s programs.
Recognizing the intelligent design in life is an antidote to the most negative ideas circulating in our culture. By contrast, seeing human beings as natural flotsam, tossed up by a random, unplanned material process without purpose or meaning, can contribute nothing positive. It only degrades and impoverishes.
The scientific debate about evolution can be set alongside the cultural impact of evolutionary thinking, uncovered in Discovery Institute documentaries like Human Zoos and The Biology of the Second Reich. A direct line connects Charles Darwin himself with these downstream effects.
In The Descent of Man, Darwin emphasized that humans are only animals in the end: “Man is constructed on the same general type or model with other mammals.” “There is no fundamental difference between man and the higher mammals in their mental faculties.” There is nothing special about our origins: “I have at least,” he wrote, “as I hope, done good service in aiding to overthrow the dogma of separate creations.” From his observations, Darwin drew the conclusion of a racial hierarchy with Africans at the bottom. He predicted that “The civilized races of man will almost certainly exterminate and replace the savage races through the world.”
Darwinian racism, with its denial of mankind’s exceptional status, flowed like a river into the textbooks that American high school biology students learned from for decades. Only the civil rights movement began to turn that river backward. Martin Luther King Jr. understood the influence of scientific materialism on scientific racism and, as Discovery Institute’s John West has noted here, he “spoke forcefully against the idea that humans are the products of a blind material process.” The design in U.S. history, which we’re living through right now, can’t be understood without due regard for this crucial background.
Photo: A tribute to George Floyd, Minneapolis, by Lorie Shaull / CC BY-SA.