Tag: black wealth gap

Fair algorithms can help us understand the causes of bias and repair society.

October 3rd, 2023
This paper examines cases of bias in AI and statistical models that affect our lives, and how we can better understand the causes of that bias and propose solutions that repair both the models and society itself.

AI and traditional statistical models are used to run search engines, dating applications, and streaming services. They are also increasingly used in hiring, college admissions, home loan applications, welfare distribution, health care provision, policing, and criminal sentencing.

We expect these algorithms to be fair. There is growing evidence that many are not.

Two examples:
- A facial recognition algorithm used by police departments in several countries falsely matched Black women’s faces ten times more often than white women’s faces, leading to more wrongful arrests of Black women.[1]
- A researcher tested Google News Feeds for gender bias using a technique that finds common associations in the data. Here are a couple examples of the associations he uncovered: man is to computer programmer as woman is to homemaker; father is to doctor as mother is to nurse.[2]
The fairness problem in the first example results from insufficient data and can be fixed by adding more pictures of Black women to the training dataset. The second example presents a more fundamental issue. There is bias in the Google News Feed data used to train the model, i.e., in the language used and news stories told that reflect traditional notions about the roles of women and men in society.

Three additional examples illustrate what happens when the data we use is biased:
- ProPublica journalists found that COMPAS, a model that forecasts whether an arrested person is likely to commit a crime if given parole, incorrectly predicted a higher recidivism rate for Blacks than for whites.[3]
- Amazon found that their model for discovering good job candidates for technology roles significantly favored men over women.[4]
- IBM found that white women were twice as likely as Black women to be diagnosed with postpartum depression despite no obvious physiological explanation for that difference.[5]
These examples have three characteristics in common.
1. The models rely on a key variable that is a flawed proxy for what we really want to measure:
  - Historical arrest rates are a bad proxy for crime rates if there is over policing in African American neighborhoods that drives higher arrest rates in those areas.
  - A training set of previously successful Amazon job candidates is a bad proxy for future success because most of Amazon’s IT employees, and 68% of the IT industry, are men, and there are likely plenty of good female candidates.[6] In fact, Amazon found that “any indicator of feminine gender identity in an application lowered the applicant’s score … a degree from a women’s college, participation in women-focused organizations, and feminized language patterns all reduced the evaluative outcome.”[7]
  - The fact that Black women have not received as much care for postpartum depression as white women is a bad predictor of whether they would benefit from that care if it were available.
2. We can sense that there are underlying societal issues causing the fairness problem in these models, namely a history of structural bias in policing, the technology industry, and health care.
3. These algorithms create a self-fulfilling prophesy, perpetuating and exacerbating bias:
  - Over policing increases arrest rates in Black neighborhoods causing the model to send more police to those neighborhoods, which in turn causes more arrests.
  - Amazon’s model favors men, so more men are hired, and the model learns it was right to rank male candidates higher than females.
  - Because African American women receive less health services, the model believes they don’t need those services, so fewer services are predicted and provided.
How can we test if models are fair and fix them if they are not?

Fairness can be measured at the group level (are women and men treated fairly?) and at the individual level (are two people from different groups treated the same, all other things being equal?). Within these categories, writers have proposed various technical definitions of “fair” and “the same,” e.g.:
- Anti-classification or fairness through blindness: A model is fair if information about a sensitive attribute (e.g., gender, race, or sexual orientation) is masked or taken out of the dataset so it can’t affect the results. From a race perspective, the model is “color blind.”
- Demographic parity: A model is fair if it selects members of protected groups at the same rate, e.g., a university’s AI model selects 10% of the Black applicants and 10% of the white applicants.
- Equalized odds: A model is fair if members of different groups exhibit the same error rates. For example, a police force’s facial recognition software incorrectly identifies 1% of white women and 1% of Black women.
These three definitions often conflict with each other. Using Amazon’s hiring algorithm:
- If Amazon masks gender, they certainly will not achieve equal selection of women and men because their dataset is dominated by men.[8]
- If they force the model to select women and men equally, they are no longer gender-blind, and they will likely fail the equalized odds test because of higher error rates for women.
So, which definition of fairness do we use? And, when the definition shows our model is not fair, how do we fix it?

Several methods have been developed to detect and mitigate bias in models and the training datasets they use. IBM, a leader in Fair AI, has developed an open-source software toolkit containing the most common of these techniques.[9] These methods often use constraints, or trade-offs, between utility and parity. In other words, we ask the model to solve for the optimal outcome – the best job candidate or university applicant – given, or constrained by, a desired level of gender or racial diversity.

Let’s take the example of mortgage loans. For many years, banks avoided lending in Black neighborhoods, a discriminatory practice known as redlining that was condoned and supported by the Federal Housing Administration and the Home Owners’ Loan Corporation.[10] Because homes are Americans’ most valuable assets,[11] the resulting lower homeownership rate for Black families has been and remains a major contributor to a 10:1 wealth gap between white and Black families in the US.[12]

Banks decide whether to lend money for a mortgage based on the creditworthiness of the applicant and other factors, most of which are related to wealth and income. Black applicants are denied a mortgage 84% more often than whites because, as with the three models discussed above, the banks’ algorithms embed and reinforce bias in lending.[13]Discrimination against Blacks leads to less homeownership, which leads to less wealth, which leads the bank’s algorithms to reject African Americans because of insufficient wealth, which makes it difficult without homeownership to amass wealth, and on and on, a phenomenon the Equal Employment Opportunity Commission (EEOC), the Civil Rights Division of the US Department of Justice, is still fighting today.[14]

How do we break this cycle? What would be a “fair” algorithm for approving mortgages given the history and persistence of lending discrimination? Should the banks’ models force higher approval rates for Black families than white families at similar wealth and income levels? If so, how should the banks set that threshold? There is nothing in the algorithms per se that tells the bank that they should approve, for example, 20% more loans for Black families than an unconstrained model would approve.

In employment cases, not bank loans, the EEOC has suggested a rule of thumb of four-fifths,[15] meaning that the percent of minority job applicants or promotion candidates selected should be no less than 80% of the percent of whites selected. But this is an arbitrary number, and employment practices are different than bank loans. And anyway, is it the bank’s job to fix societal discrimination?

There is a relatively new line of research on fair algorithms that may help us avoid arbitrary fixes, and also help answer the question of who should pay to repair bias when we find it. In a word, causation.

Remember that sense we had that something else was operating in the background of the unfair models, those underlying structural problems with policing, the IT industry, and health care that seemed to be causing the persistent bias we detected? How can we mitigate that bias if we don’t understand and measure those causes?

In Causal Reasoning for Algorithmic Fairness, Loftus, et al. state:

This approach [causal models] provides tools for making the assumptions that underlie intuitive notions of fairness explicit. This is important, since notions of fairness that are discordant with the actual causal relationships in the data can lead to misleading and undesirable outcomes. Only by understanding and accurately modelling the mechanisms that propagate unfairness through society can we make informed decisions as to what should be done.[16]

How would this work in practice? A group of researchers from MIT, IBM and Microsoft provide us with an example using the mortgage discrimination problem. They develop two case studies:

The first case study shows how institutionalized redlining, which denied mortgage insurance to neighborhoods that Black people and immigrants most lived in from the 1940s to 1960s, affected the accumulation of wealth through housing.

Then, the second case study develops an ML [machine learning] system that shows the estimated cost of reparations if we wish to intervene in today’s mortgage lending to address historical harms.^[17]

In other words, the first algorithm models the causes of discrimination in lending; the second tells us how much it would take to repair those wrongs. The answer: to change 39,000 rejections of bank prime loans to accept would require average support to Black applicants of $41,256 for the downpayment and $205/month for the mortgage. For an FHA loan, no downpayment support is required and the average monthly subsidy would be $578. These results are specific and transparent, based on models that can be demonstrated and reproduced, not arbitrary criteria like the 4/5^th rule. The result can be readily compared, for example, to the housing grants of $25,000 the city of Evanston provides as reparations to the descendants of slavery.[18]

Now, admittedly, housing discrimination lends itself to causal analysis. There is a lot of data, the history has been thoroughly studied, and the causal links are strong and self-perpetuating. Perhaps other forms of racial, gender or sexual-orientation discrimination would be tougher to model. Or perhaps we are simply waiting for the right people to take on the challenge!

Who are the right people? Unlike the previous bias mitigation techniques, causal models require domain knowledge to posit and test how and why bias occurs, not simply observe it. That means data scientists building AI algorithms must work with experts from civil society and universities.

We should also add lawyers. Could the affirmative actions proposed by these causal models survive court scrutiny, even the current Supreme Court?

What about SCOTUS?

US civil rights law generally defines two types of discrimination: disparate treatment and disparate impact. For our purposes, disparate treatment would be intentionally creating models that discriminate against protected groups, and then acting on the results. Disparate impacts are unintended outcomes that are discriminatory.

In our cases, these two concepts are often in conflict. We are purposely creating models that treat two protected classes differently (disparate intent) to reduce disparate impacts. Is that legal?

In two decisions, Steelworkers v. Weber and Johnson v. Transportation Agency,[19] the Supreme Court defined criteria employers could use to purposely favor minorities or other protected groups, i.e., use affirmative action to address historical discrimination:
- Employers must be responding to a “manifest imbalance” in “traditionally segregated job categories”, i.e., reacting to a clear case of historical discrimination.
- They must have an affirmative action plan in place before they act.
- That plan should not “unnecessarily trammel” the rights of others.
- The actions should be temporary.
The mortgage loan example satisfies these criteria:
- The causal models clearly demonstrate, and quantify, manifest imbalances.
- Any reparative action to affect the racial wealth gap would likely require a law like the one Evanston passed, and the ones Congress, California, New York, and a dozen cities are considering. These would be affirmative action plans embedded in the law.
- If the government provides direct subsidies to Black families, the models would not change the outcomes for other races, just for African Americans.
- Any reparative actions would be temporary (perhaps a couple generations), until the harm is mitigated.
But these two cases are over 30 years old, and the current Supreme Court is much more skeptical about affirmative action programs. In its recent decision outlawing affirmative action in university admissions, the Supreme Court went further, emphasizing three additional problems with the affirmative action programs at Harvard and University of North Carolina:
- “The interests that respondents view as compelling cannot be subjected to meaningful judicial review.”
- “Respondents’ admissions programs fail to articulate a meaningful connection between the means they employ and the goals they pursue.”
- “They require stereotyping … When a university admits students on the basis of race, it engages in the offensive and demeaning assumption that [students] of a particular race, because of their race, think alike.”[20]
The first two points are precisely the reason for causal algorithms: so that the interests of society can be studied and mapped, and so that the connection between the means and the ends can be defined and quantified. The third point does not apply to our mortgage loan example. No one is saying Black families all think alike. Just that they have been discriminated against in homeownership.

So, in summary, even in the current environment, these explicit causal models may provide a defense against judicial scrutiny.

What if…

AI is all the buzz these days. Everyone is writing machine-learning algorithms to do everything. AI will apparently save the stock market, and even the economy.[21] Governments are trying to legislate.[22] The public is concerned.[23]

We have seen how these models make decisions that can affect some of the most important aspects of our lives – getting a job, a mortgage, or a spot at a good university – and how they often embed and reinforce the biases present in society.

What if the IT industry, universities, and nonprofits devoted a portion of their resources, say one-half of one percent, to developing models that detect and repair bias, and shared their results with the public? How would that information change perceptions, practices, and policy? Could knowing more about the causes of discrimination and how to fix them help us reduce the STEM gap,[24] the Black wealth gap, or the political chasm that divides Americans? It’s worth a try.

[1] The best algorithms struggle to recognize black faces equally.

[2] Man is to computer programmer as woman is to homemaker?

[3] How we analyzed the COMPAS recidivism algorithm.

[4] Amazon scraps secret AI recruiting tool that showed bias against women.

[5] IBM researchers investigate ways to help reduce bias in healthcare AI.

[6] Women in tech statistics highlights.

[7] Algorithmic reparation.

[8] In fact, “color blind” anticlassification has been shown time and again to be nothing of the sort. People who want to discriminate simply find a strong proxy for race, e.g., ZIP codes in highly segregated cities.

[9] IBM’s AI Fairness 360 toolkit.

[10] See The Federal Reserve’s History of Redlining for the Federal Reserve’s brief history of redlining.

[11] Cleveland Fed’s Evaluating Homeownership as the Solution to Wealth Inequality.

[12]In 2017, mean white family wealth in the US was 10 times higher than mean Black family wealth. See How wealth inequality has changed in the U.S. since the Great Recession, by race, ethnicity and income.

[13] Black mortgage applicants are denied 84% more often than whites.

[14] See Justice Department Announces New Initiative to Combat Redlining for a description of a Department of Justice Civil Rights Division anti-redlining initiative launched in 2021.

[15] Questions and Answers to Clarify and Provide a Common Interpretation of the Uniform Guidelines on Employee Selection Procedures.

[16] Causal Reasoning for Algorithmic Fairness.

[17] Beyond Fairness: Reparative Algorithms to Address Historical Injustices of Housing Discrimination in the US.

[18] City of Evanston Reparations.

[19] US Supreme Court Steelworkers v. Weber and US Supreme Court Johnson v. Transportation Agency.

[20] US Supreme Court Students for Fair Admissions, Inc. v. President and Fellows of Harvard College.

[21] CNBC The AI explosion could save the market and maybe the economy.

[22] Harvard Business Review Who is going to regulate AI?

[23] Pew Research Awareness of artificial intelligence in everyday activities.

[24] The STEM Gap: Women and Girls in Science, Technology, Engineering and Mathematics.
The case for reparations for the descendants of slavery

September 10th, 2023
Frederick Douglas, Republican National Convention, 1876:

When the Russian serfs had their chains broken and were given their liberty, the government of Russia—aye, the despotic government of Russia—gave to those poor emancipated serfs a few acres of land on which they could live and earn their bread. But when you turned us loose, you gave us no acres: you turned us loose to the sky, to the storm, to the whirlwind, and, worst of all, you turned us loose to the wrath of our infuriated masters.

The Oxford dictionary defines reparations as “the making of amends for a wrong one has done, by paying money to or otherwise helping those who have been wronged.” Courts routinely assess monetary awards for damages suffered. Indigenous people in the US and Canada, Japanese-Americans interned during WWII, and holocaust survivors of Nazi concentration camps have received reparations. In the last of these examples, German restitution not only helped build the state of Israel but served as one element of a process of education and healing that is visible today: according to the Anti-Defamation League, 15% of Germans hold anti-Semitic views vs. 30% in Europe as a whole.[1] As Israel’s first Prime Minister, David Ben-Gurion, said:

For the first time in the history of relations between people, a precedent has been created by which a great State, as a result of moral pressure alone, takes it upon itself to pay compensation to the victims of the government that preceded it. For the first time in the history of a people that has been persecuted, oppressed, plundered and despoiled for hundreds of years in the countries of Europe, a persecutor and despoiler has been obliged to return part of his spoils and has even undertaken to make collective reparation as partial compensation for material losses.[2]

The case for reparations for African Americans is clear and compelling. Africans were brought in chains to the New World; held in bondage for 250 years; subjected to the Jim Crow racial caste system in the South for another 100 years, and continue to face a legacy of housing, education, employment and criminal justice discrimination to this day. Some examples:
- At southern Senators’ insistence, Social Security, America’s single most important benefits program, excluded farm workers and domestics, making 75% of Black workers in the South and 60% of all Black workers in the US ineligible.
- The GI Bill provided subsidized loans for 30% of all homes purchased in the 10 years after WWII and higher education for half of WWII veterans. Local implementation of the GI Bill meant that, in Southern States like Mississippi where in 1947 only two of 3,229 GI Bill home loan recipients were African American, Kathleen J. Frydl writes that “it is more accurate to say that blacks could not use this particular title.”[3] The North was not much better. In the same period, only 100 of 67,000 GI Bill-backed mortgages in New York and New Jersey went to non-Whites.[4] Black veterans also found it nearly impossible to attend white universities: 95% of the few African American vets who secured GI Bill loans attended overcrowded, underfunded, and segregated Black universities.[5]
- Redlining, the Home Owners’ Loan Corporation and Federal Housing Administration practice of denying government-backed housing loans in minority neighborhoods, was outlawed over 50 years ago but its impact lives on. A recent study found that 64% of neighborhoods redlined 80 years ago remain minority neighborhoods today and 74% remain poor.[6]
Thus, the two most important drivers of wealth – education and home ownership – and the most important benefits program in US history – social security – were routinely denied to African Americans.

The legacy of these and other injustices can be seen in many economic and social indicators today, including incarceration rates (5X higher for Blacks than Whites), unemployment (2X higher for Blacks), and family income (35% lower for Blacks). Of these indicators, the clearest sign of the lasting legacy of slavery, segregation and racism is the African American family’s lack of wealth:
- The median Black family in America has only one-tenth the wealth of their White counterpart ($17,000 vs. $171,100).[7]
- Only 42% of African American families own their homes compared to 73% of Whites. In fact, Black college graduates are less likely to own a home than White high school dropouts.[8]
Today, 60 years after the passage of the Civil Rights Act, Black families struggle to accumulate enough wealth to pass it down to the next generation: to bequeath a home to a daughter or pay for a grandson’s college education.

As William Darity Jr. and A Kirsten Mullen find[9], this inability to accumulate wealth is not the result of bad behavior. None of the oft cited “causes” of Black poverty can explain the wealth gap:
- Single parent families: single white parents have more than 2X the wealth of married black parents.
- Profligacy: At similar income levels, Blacks’ savings rates are the same as Whites’.
- Educational attainment: At similar income levels, Blacks obtain more years of schooling than Whites. But single Black household heads with a university degree have less net worth than Whites who never finished high school, and Black women with a college degree have a median net worth of $11,000 vs. $384,000 for their White counterparts.
And it is not getting any better. Over the last 55 years:
- Black homeownership has only increased 1%
- Black household income has remained between 55% and 60% of White household income.
One hundred and fifty years after the end of slavery, the wealth gap is still 10 to 1. Neither legislation, nor affirmative action, nor protest has made a difference. Shall we wait another 150 years to see if anything changes, or should we repair this wrong?

How much?

A number of methods have been proposed to calculate the size of reparations for descendants of slavery. These include estimating the present value of:
- The income slaves would have earned if they had been free
- The wealth their labor created
- The land they were promised but never received
- The income Blacks would have earned after slavery had there been no discrimination
These estimates generally range from hundreds of billions to tens of trillions of dollars.

A metric that is arguably the best representation of the impact of years of oppression and discrimination on Black families is the wealth gap between White and Black families.[10] The difference between mean family wealth – $934,000 for Whites and $138,000 for Blacks – times the number of Black households yields $13 trillion, or $318,000 for each African American.

This might sound like an impossibly large number, but in fact it is roughly equal to the cost of the COVID stimulus packages plus President Biden’s three big spending bills. Paid out over 20 years, the annual amount, $650 billion, would equal only 10% of the federal budget and be roughly the size of the annual American subsidy to the fossil fuel industry.^[11]

This amount could be funded by the Federal Reserve (between March 2020 and March 2023 the Fed created $5 trillion through quantitative easing), by running a deficit (the 2022 federal deficit was $1.4 trillion) or by increasing taxes (a 13% increase would be required). The first two methods would be inflationary, but only to the extent that these payments fail to create new value in the economy.

For whom? How?

The California reparations commission recommends compensation to African Americans who can identify a Black descendant living in the US prior to 1900. We should also consider people who cannot track their ancestry back that far given the damage done to Black families by continued discrimination in the 20^th and 21^st centuries.

Experts argue for a variety of compensation methods including direct payments and grants for homeownership, education and entrepreneurship. Payments could be spread out over time. In The Case for Black Reparations Boris Bittker proposes 20 years.^[12] Such a long period gives time for intergenerational dynamics and for the agencies managing the program to try different models. As with any big program, e.g., the New Deal or the War on Poverty, experimentation is key, adjusting as information on outcomes becomes available. The program must also include significant funds for communications, outreach, atonement, and education. As with the case of Germany discussed above, successful reparations efforts must improve society, not just compensate for past wrongs.

The history of reparations in the United States

Individual freed slaves have demanded, and in a few cases won, compensation for their enslavement starting as early as 1783. The most famous attempt to compensate ex-slaves was General William Sherman’s land grant of 40 acres (and a surplus government mule when available) to freed slave families. After Lincoln’s assassination, however, President Andrew Johnson quickly rescinded that order stating, “This is a country for white men and, by God, as long as I am President, it shall be a government for white men.” After the failure of Reconstruction, the question of reparations was rarely raised until the Civil Rights era, and then with little success.

Then, beginning in the late 1980s, legislatures, courts and commissions began exploring massacres of Blacks in Rosewood Florida (1923), Tulsa Oklahoma (1921) and Wilmington North Carolina (1898). Law suits were brought against the US Department of Agriculture and Prince Edward County, Virginia (the latter because the County shut down its public schools for 5 years beginning in 1959 to avoid desegregation, providing vouchers to White children). All of these cases were successful in that they concluded that genuine, egregious harm had been done, but very little money changed hands.

Why now?

Legislatures are beginning to act

Beginning in 1989 and every year after that for 28 years, Representative John Conyers introduced a bill in the US House of Representatives, HR 40 (for “40 acres and a mule”), to create a commission to study the question of reparations for the ancestors of slaves. Finally, in 2021, it was successfully voted out of the House Judiciary Committee. With over 200 cosponsors, it has a real chance of passing if Democrats take back the House in 2024. As always on civil rights issues, the Senate is a tougher challenge but, if the House passes HR 40, the 40 Senators currently supporting a similar bill might be able to pass it if they time debate to coincide with the next inevitable spate of police shootings, particularly since the bill only studies the question of reparations.

Thirteen cities have commissions studying reparations. One, Evanston Illinois, has begun compensating African Americans. In June, New York passed a bill to create its commission and the California commission issued its recommendations. The California report concluded that reparations are required, proposed mechanisms for calculating compensation, and described programs that should be implemented. The state legislature will likely take up the report in the Fall.

Perceptions are beginning to change

Black Lives Matter, the New York Times 1619 Project, reparations commissions, press reports, campus debate: people are talking about race in America. And, despite White backlash since 2020 and the Supreme Court’s actions against affirmative action, there are signs that old perceptions are changing. According to Pew Research, between 2002 and 2021, the percent of Americans that support payments to the descendants of slaves more than doubled from 14% to 30%.[13] While certainly not a majority, this is progress nonetheless.

In 2019, the Economist published a set of charts under the title, “Societies change their minds faster than people do.” The charts made three points:
- Demographic shifts are often more important than changes in individuals’ views, as shown by the declining impact of the “Greatest generation” in the chart below.
- This can result in rapid changes in societal perceptions, e.g., a 45-point increase in support for same-sex marriage within 18 years.
- Like support for gay marriage, support for more government spending to improve Black lives “has surged.” (See the chart to the right).
What would it take to build on that surge to create a consensus? A national dialogue, which is exactly what would happen if Congress passed HR 40. Passage of this bill is a critical first step.

This paper opened with a quote from Frederick Douglas in 1865. In response to those who believe a focus on Black reparations is inappropriate because it ignores poor Whites, the paper closes with a quote from Lyndon Johnson nearly 100 years later:

Negro poverty is not white poverty. Many of its causes and many of its cures are the same. But there are differences – deep, corrosive, obstinate differences – radiating painful roots into the community and into the family, and the nature of the individual. These differences are not racial differences. They are solely and simply the consequence of ancient brutality, past injustice, and present prejudice.[14]

We can repair Black families and, in so doing, repair America.

[1] How Germany paid reparations for the Holocaust (qz.com).

[2] https://www.theatlantic.com/magazine/archive/2014/06/the-case-for-reparations/361631/

[3] Frydl, Kathleen, The GI Bill, Cambridge University Press, 2009.

[4] https://www.bostonglobe.com/2022/02/23/opinion/gi-bill-was-one-worst-racial-injustices-20th-century-congress-can-fix-it/.

[5] Brown, Nikki L.M. and Barry M. Stentiford, The Jim Crow Encyclopedia, Greenwood Publishing Group, 2008.

[6] https://ncrc.org/holc/.

[7] https://www.pewresearch.org/short-reads/2017/11/01/how-wealth-inequality-has-changed-in-the-u-s-since-the-great-recession-by-race-ethnicity-and-income/.

[8] https://www.urban.org/events/black-homeownership-gap-research-trends-and-why-growing-gap-matters.

[9] Darity, William and A Kirsten Mullen, From Here to Equality: Reparations for African Americans in the Twenty-first Century, University of North Carolina Press, 2020.

[10] The mean is more appropriate than the median because it reflects the total value earned by all Whites and Blacks, including the very wealthy.

[11] Still Not Getting Energy Prices Right: A Global and Country Update of Fossil Fuel Subsidies (imf.org).

[12] Bittker, Boris, The Case for Black Reparations, Beacon Press, 1972.

[13] https://www.pewresearch.org/short-reads/2022/11/28/black-and-white-americans-are-far-apart-in-their-views-of-reparations-for-slavery/.

[14] https://www.presidency.ucsb.edu/documents/commencement-address-howard-university-fulfill-these-rights.

Tag: black wealth gap

Fair algorithms can help us understand the causes of bias and repair society.

The case for reparations for the descendants of slavery

We can repair Black families and, in so doing, repair America.