Deborah Hellman

Diversity by Facially Neutral Means

The decision in Students for Fair Admissions v. President & Fellows of Harvard College (SFFA), invalidating the use of race in college admissions, reignites a pressing and critical question. Is the deliberate use of facially neutral means to achieve racial diversity constitutionally permissible? The problem is that current equal protection doctrine suggests conflicting answers to this question. On one hand, cases addressing the use of explicit racial classifications state clearly that the use of race is impermissible if diversity could be achieved by facially neutral means. These statements suggest that universities and others may adopt such means. On the other hand, when state actors adopt facially neutral policies that have a disparate negative impact on the basis of race or sex, these policies are impermissible if enacted specifically because they will have this effect. This part of the doctrine suggests that state actors may not adopt facially neutral means of achieving racial diversity if they do so in order to achieve this result.

This Article resolves that enduring puzzle. It does so by explaining that equal protection doctrine contains two distinct commitments: a prohibition on race-based differential treatment and a prohibition on governmental actors intending to harm. The claim that the combination of these commitments—the intent to select on the basis of race—is also forbidden rests on a mistake. Because these two doctrinal threads stem from different normative foundations, they cannot be combined. In addition, while intentions do matter within equal protection doctrine, that observation is overstated. It is only the intent to harm that is constitutionally relevant. Facially neutral policies aimed at increasing racial diversity lack an intent to harm and are therefore permissible.

Justice Kagan: So suppose that . . . there’s a 10 percent plan or something like that, and part of the justification is socioeconomic diversity and another part of the justification is we’ll also get more racial diversity in this manner. . . . Is that permissible?

Mr. Strawbridge: Well, like I said, it—it’s a different analysis when the . . . mechanism that’s chosen is not a racial classification itself, but I do think that this Court’s precedents—

Justice Kagan: Well, I guess the question is why—why is that true. A lot of our constitutional doctrine suggests that it’s not a different analysis. In other words, one way you can offend the Constitution is by using an impermissible classification. Another way you can offend the Constitution is by devising a proxy mechanism with the purpose of . . . achieving the same results that the impermissible classification would.

Mr. Strawbridge: Right.1 1.Transcript of Oral Argument at 13–14, Students for Fair Admissions, Inc. v. President & Fellows of Harvard Coll., 143 S. Ct. 2141 (2023) (No. 21-707).Show More

Introduction

The Supreme Court’s decision in Students for Fair Admissions, Inc. v. President & Fellows of Harvard College (SFFA),2 2.143 S. Ct. 2141 (2023).Show More striking down the use of race-based classifications in university admissions, sets up an important question.3 3.The use of race in admissions was invalidated because the interests allegedly served by student body diversity are too difficult to measure and because the means adopted to achieve these ends are inapt. Id. at 2166–67. Additionally, in the Court’s view, the policies at issue rely on racial stereotypes and harm those not preferred by the policies. Id. at 2168–70. Chief Justice Roberts’s opinion for the Court does not, however, explicitly overrule Grutter v. Bollinger, 539 U.S. 306 (2003), and explicitly leaves open the question of whether the consideration of race is also impermissible in the context of admissions at the military academies. SFFA, 143 S. Ct. at 2166 n.4 (“This opinion also does not address the issue, in light of the potentially distinct interests that military academies may present.”).Show More May universities adopt “facially neutral”4 4.The term “facially neutral” refers to a law or policy that does not explicitly classify on protected grounds.Show More selection policies that will predictably increase racial diversity if they do so because these policies are likely to have this result?5 5.The Court in SFFA considered both a constitutional claim and a statutory claim but viewed the prohibition on race discrimination contained in Title VI of the Civil Rights Act of 1964 as equivalent to the constitutional requirements of equal protection. For this reason, the result of the constitutional analysis will also have implications for non-state actors. See SFFA, 143 S. Ct. at 2156 n.2 (explaining that the Court would “evaluate Harvard’s admissions program under the standards of the Equal Protection Clause” because “discrimination that violates the Equal Protection Clause of the Fourteenth Amendment committed by an institution that accepts federal funds also constitutes a violation of Title VI” (quoting Gratz v. Bollinger, 539 U.S. 244, 276 n.23 (2003))).Show More Such policies might include admissions guarantees by a state university to the top ten percent of each high school senior class in the state, as Justice Kagan noted in the oral argument of the University of North Carolina case,6 6.See Transcript of Oral Argument at 13–14, SFFA, 143 S. Ct. 2141 (No. 21-707).Show More or “plus” factors for students who are first-generation college students, who attend under-resourced schools, or whose families are poor or are the descendants of enslaved people,7 7.See id. at 13, 43–45.Show More among others.

It may seem like the answer is an obvious “yes,” as Mr. Strawbridge, the lawyer for Students for Fair Admissions, initially suggested.8 8.Id. at 13. Mr. Strawbridge seems to equivocate in his answer to Justice Kagan’s question. He initially seems to say that such policies would be permissible but ultimately does say that if the university adopted the policy at least in part because it would increase diversity and would not have adopted it without this reason, then the facially neutral policy would be impermissible. Id. at 16 (“[I]f the only reason to do it is through the narrow lens of race and there is no other race-neutral justification for it that the government can come forward and demonstrate that would have led it to adopt that policy anyway, I think . . . that’s the only scenario where it would create problems under the Court’s precedent.”).Show More After all, in the affirmative action cases that predate the current decision, narrow tailoring was assessed by reference to whether race-neutral means of achieving diversity were available.9 9.See, e.g., Wygant v. Jackson Bd. of Educ., 476 U.S. 267, 280 n.6 (1986); Grutter v. Bollinger, 539 U.S. 306, 340 (2003) (“We are satisfied that the Law School adequately considered race-neutral alternatives currently capable of producing a critical mass without forcing the Law School to abandon the academic selectivity that is the cornerstone of its educational mission.”); id. at 342 (“Universities in other States can and should draw on the most promising aspects of these race-neutral alternatives as they develop.”).Show More Indeed, the Justices who dissented in those previous cases did so in part on the grounds that such alternatives were present, in their view.10 10.See, e.g., Fisher v. Univ. of Tex. at Austin (Fisher II), 579 U.S. 365, 426–27 (2016) (Alito, J., dissenting) (noting that if the University of Texas at Austin adopted race-neutral policies it could achieve diversity “without injecting race into the process”); Grutter, 539 U.S. at 394–95 (Kennedy, J., dissenting) (“Were the courts to apply a searching standard to race-based admissions schemes, that would force educational institutions to seriously explore race-neutral alternatives. . . . Other programs do exist which will be more effective in bringing about the harmony and mutual respect among all citizens that our constitutional tradition has always sought. They, and not the program under review here, should be the model, even if the Court defaults by not demanding it.”); Gratz, 539 U.S. at 297 (Souter, J., dissenting) (“Drawing on admissions systems used at public universities in California, Florida, and Texas, the United States contends that Michigan could get student diversity in satisfaction of its compelling interest by guaranteeing admission to a fixed percentage of the top students from each high school in Michigan. . . . [T]here is nothing unconstitutional about such a practice . . . .”).Show More If the explicit use of race is not narrowly tailored to achieve the educational benefits of diversity when workable race-neutral means exist to achieve the same result, surely the deliberate use of such race-neutral means is constitutionally permissible.11 11.Judge Heytens makes precisely this argument in response to the claim that the deliberate use of race-neutral polices to increase racial diversity transforms the policy into one that is race-based. See Coal. for TJ v. Fairfax Cnty. Sch. Bd., 68 F.4th 864, 891 (4th Cir. 2023) (Heytens, J., concurring) (emphasizing that “it would be quite the judicial bait-and-switch to say such race-neutral efforts are also presumptively unconstitutional” after spending “decades telling school officials they must consider race-neutral methods for ensuring a diverse student body before turning to race-conscious ones”), cert. denied, No. 23-170, 2024 WL 674659 (U.S. Feb. 20, 2024).Show More

And yet, scholars have long wondered about the constitutional permissibility of such policies.12 12.See, e.g., Brian T. Fitzpatrick, Can Michigan Universities Use Proxies for Race After the Ban on Racial Preferences?, 13 Mich. J. Race & L. 277, 283 (2007) (arguing that “antidiscrimination laws have been [and should be] interpreted to prohibit not only facial classifications, but also neutral classifications that were adopted to serve as proxies for the facially-prohibited one”); Richard A. Primus, Equal Protection and Disparate Impact: Round Three, 117 Harv. L. Rev. 493, 496 (2003) (exploring the possibility that disparate impact liability mandated by Title VII may violate the constitutional guarantee of equal protection because that statute was adopted with the purpose of redressing racial inequality); Kim Forde-Mazrui, The Constitutional Implications of Race-Neutral Affirmative Action, 88 Geo. L.J. 2331, 2364–65, 2377 (2000) (arguing that race-neutral affirmative action may be unconstitutional if motivated by the same purposes as affirmative action that relies on racial classification). See generally Larry Alexander & Kevin Cole, Discrimination by Proxy, 14 Const. Comment. 453 (1997) (assuming that the deliberate use of a facially neutral trait to select for people with a protected trait violates the anti-discrimination principle and arguing that equal protection doctrine is internally conflicted).Show More To see the argument for this position, imagine a hypothetical case in which a university admissions program made minority race a minus rather than a plus. After being sued for race discrimination, the university stops using race explicitly in its admissions process. Instead, suppose it adopts a policy disfavoring people from particular zip codes. Further, suppose the university does so because race correlates with zip code, so the university can achieve the same result as it had by explicitly disfavoring people of particular races. If the deliberate use of zip codes to exclude Black students would be impermissible, then should the deliberate use of class rank to include Black students be treated similarly? It is precisely this logic that Justice Kagan referred to in her question to Mr. Strawbridge.

The argument for treating these hypothetical cases the same seems plausible for two reasons. First, when facially neutral policies disadvantage women or racial minorities, the Court evaluates these policies by reference to the reasons for which they were adopted. If the state chose the facially neutral policy “because of” its effect, then the policy is treated as if it contains an explicit classification.13 13.Pers. Adm’r of Mass. v. Feeney, 442 U.S. 256, 279 (1979) (emphasis added) (requiring a showing that a defendant discriminated “‘because of,’ not merely ‘in spite of,’ its adverse effects upon an identifiable group” in order to impose liability). The Feeney Court upheld Massachusetts’s preference for veterans in civil service positions, despite the fact that the policy disproportionately excluded women, because the policy was not adopted in order to exclude women. Id. at 275. The clear implication of this holding is that had the policy been adopted in order to exclude women, it would be impermissible.Show More Second, when explicit classifications are used, the Court has treated the fact that the policy was well-motivated as legally insignificant, and has treated a benefit on the basis of race in the same manner as a burden.14 14.The Chief Justice emphasizes this fact in SFFA. See SFFA, 143 S. Ct. 2141, 2175 (2023) (critiquing the dissent by asserting that “[w]hile the dissent would certainly not permit university programs that discriminated against black and Latino applicants, it is perfectly willing to let the programs here continue”).Show More For example, the explicit use of race in higher education admissions must pass strict scrutiny even when the policy is adopted for benign or even laudable reasons.15 15.See id. at 2166; Regents of the Univ. of Cal. v. Bakke, 438 U.S. 265, 298–99 (1978) (holding that whether a racial classification is used for benign purposes or invidious ones is not relevant and instead that classifications that “touch upon an individual’s race or ethnic background” all require the application of strict scrutiny).Show More Moreover, in SFFA, Chief Justice Roberts emphasized that university admissions is a “zero-sum” enterprise, and so, in his view, giving a plus to some applicants necessarily functions as a minus to others.16 16.SFFA, 143 S. Ct. at 2169 (“A benefit provided to some applicants but not to others necessarily advantages the former group at the expense of the latter.”).Show More According to the combined logic of these two features of the doctrine, the deliberate use of class rank to select for minority applicants would seem to be constitutionally impermissible.

The Court’s opinion in SFFA itself does not directly address the question of whether universities may use race-neutral means to achieve diversity.17 17.In another case, the Chief Justice has suggested that he would find such motivation suspect. See, for example, Chief Justice Roberts’s comment in Parents Involved,where he opined that “[r]acial balancing is not transformed from ‘patently unconstitutional’ to a compelling state interest simply by relabeling it ‘racial diversity,’” which suggests that he might find intending to select for people on the basis of race to also be a patently unconstitutional motivation. Parents Involved in Cmty. Schs. v. Seattle Sch. Dist. No. 1, 551 U.S. 701, 732 (2007).Show More However, Justice Kavanaugh, concurring, indicated that he would find it constitutional for a university to do so: “[G]overnments and universities still ‘can, of course, act to undo the effects of past discrimination in many permissible ways that do not involve classification by race.’”18 18.SFFA, 143 S. Ct. at 2225 (Kavanaugh, J., concurring) (quoting City of Richmond v. J.A. Croson Co., 488 U.S. 469, 526 (1989) (Scalia, J., concurring)).Show More And Justice Gorsuch noted that “Harvard could nearly replicate the current racial composition of its student body without resorting to race-based practices if it: (1) provided socioeconomically disadvantaged applicants just half of the tip it gives recruited athletes; and (2) eliminated tips for the children of donors, alumni, and faculty.”19 19.Id. at 2215 (Gorsuch, J., concurring).Show More The implication of this comment is that it would be permissible for Harvard to do so, even if it is motivated to adopt these policies to replicate the current racial composition of the class. Lastly, Justice Thomas, concurring, used language that suggests that he too would find the use of race-neutral policies to achieve a diverse student body permissible. He wrote: “Race-neutral policies may thus achieve the same benefits of racial harmony and equality without any of the burdens and strife generated by affirmative action policies.”20 20.Id. at 2206 (Thomas, J., concurring).Show More That said, none of these statements specifically address whether race-neutral policies deliberately adopted to achieve racial diversity would be constitutional.21 21.What Justice Thomas would say about such a case is especially uncertain, as other parts of his concurring opinion suggest that he finds questionable the very idea that racial diversity is important in higher education. See id. at 2207.Show More

The argument that they are not constitutional has recently gained traction. For example, it has already appeared in debates about the admissions criteria for public magnet schools.22 22.For a recent analysis, see Sonja Starr, The Magnet School Wars and the Future of Colorblindness, 76 Stan. L. Rev. 161, 163–64 (2024). Show More In Coalition for TJ v. Fairfax County School Board, a change from one facially neutral admissions policy to another at the Thomas Jefferson High School for Science and Technology (“TJ”) was challenged on the grounds that the change was motivated by a desire to “racially balance” the school.23 23.See Coal. for TJ v. Fairfax Cnty. Sch. Bd., 68 F.4th 864, 871–72, 875–76 (4th Cir. 2023). Judge Rushing, dissenting, argued that a “school board’s motivation to racially balance its schools, even using the means of a facially neutral policy, must be tested under exacting judicial scrutiny.” Id. at 893 (Rushing, J., dissenting).Show More While one could interpret that claim as alleging that the School Board intended to exclude Asian students, which would be prohibited, it could also be understood as a claim that the School Board impermissibly changed its policy in order to include more Black and Latinx students. In its petition for certiorari, the petitioners fused these questions and treated these claims as equivalent.24 24.See Petition for Writ of Certiorari at 13, Coal. for TJ, No. 23-170 (U.S. Aug. 21, 2023) (styling their first reason for granting the petition in these terms: “The Use of Facially Race-Neutral Admissions Criteria to Achieve Racial Balance Presents an Unsettled Question of National Importance”).Show More To date, this argument has not succeeded,25 25.The majority opinion in Coalition for TJ does not specifically address this issue. See generally Coal. for TJ, 68 F.4th 864. In Christa McAuliffe Intermediate School PTO, Inc. v. de Blasio, the plaintiffs unsuccessfully argued that “where the government shifts to a policy that treats applicants differently based on a factor that is designed to operate as a proxy for race, it becomes suspect.” 627 F. Supp. 3d 253, 263–65 (S.D.N.Y. 2022).Show More but it has not been repudiated, either.26 26.See, for example, Lewis v. Ascension Parish SchoolBoard, 662 F.3d 343, 352 (5th Cir. 2011) (per curiam), in which the Fifth Circuit reversed the lower court’s summary judgment and remanded the case in light of the existence of genuine issues of material fact regarding whether a racially discriminatory purpose and effect were present. Then-Chief Judge Jones, concurring, asserted that geographic boundaries used in student assignment are not racially neutral if adopted for the purpose of achieving racial balance in the schools. Id. at 354 (Jones, C.J., concurring) (“Streets . . . may well be racial proxies because the district or its agents apparently knew and used the racial composition of the people living on those streets to pursue racial balancing.”). Following remand to the district court, the Fifth Circuit upheld the finding of no constitutional violation without reaching the question of whether discriminatory intent was present. Lewis v. Ascension Parish Sch. Bd., 806 F.3d 344, 358, 363 (5th Cir. 2015).Show More And, while the Supreme Court declined to grant certiorari in this case, its reasons for doing so are unknown.27 27.Coal. for TJ v. Fairfax Cnty. Sch. Bd., No. 23-170, 2024 WL 674659, at *1 (U.S. Feb. 20, 2024). Justice Alito, joined by Justice Thomas, dissented from the denial of certiorari but did so for reasons unrelated to the question regarding what intentions are prohibited under equal protection doctrine. Id. at *1, 5 (Alito, J., dissenting from the denial of certiorari).Show More Perhaps the Court approved of the reasoning of the U.S. Court of Appeals for the Fourth Circuit that the use of facially neutral policies to increase diversity is permissible, or perhaps the Court was simply not yet ready to take this issue on.

This unresolved question comes with high stakes. If the Court decides that facially neutral policies adopted to increase racial diversity in public magnet schools are unconstitutional, many policies, both within the education context and beyond it, would also be at risk. For example, suppose a universal pre-kindergarten program is adopted in order to reduce racial disparities in educational achievement. Or suppose a city adopts a government-funded doula program in order to reduce the racial disparity in maternal death rates.28 28.See, e.g., Zeina Mohammed, Black Women Are More Likely to Die During Pregnancy. A Local Doula Program Aims to Change That, Bos. Globe (Dec. 30, 2022, 5:14 PM), https://www.bostonglobe.com/2022/12/30/metro/mass-general-brighams-doula-program-aim‌s-close-racial-disparities-childbirth/; Working Together to Reduce Black Maternal Mortality, Ctrs. for Disease Control & Prevention, https://www.cdc.gov/womens-health/features/matern‌al-mortality.html [https://perma.cc/4TR9-VV7E] (last updated Apr. 8, 2024).Show More If the intention to affect which racial group is most benefited by a policy constitutes an impermissible intent, these programs would be subject to strict scrutiny. While such policies could be adopted for alternative reasons, unrelated to the race of likely beneficiaries, if they would not have been adopted for these alternative reasons alone, then the constitutional problem remains.29 29.Where a constitutionally impermissible reason is a motivating reason for facially neutral state action, the burden shifts to the state to show that the same decision would have been reached absent the impermissible motivation. If the state is unable to do so, the facially neutral policy is subject to heightened review. See Village of Arlington Heights v. Metro. Hous. Dev. Corp., 429 U.S. 252, 265–66, 270 n.21 (1977).Show More

Moreover, the implications of invalidating facially neutral policies enacted to improve racial diversity or reduce racial disparities likely apply beyond the constitutional context. Because the Court in SFFA treats the requirements of Title VI of the Civil Rights Act of 1964 as equivalent to the requirements of equal protection, prohibitions on the deliberate adoption of facially neutral means of achieving diversity could also extend to non-state actors receiving federal funds.30 30.See supra note 5.Show More

This Article thus addresses a pressing and critical question. To restate it in terms of the two hypothetical policies mentioned earlier, we need to know: What, if anything, distinguishes the use of class rank to include minority students from the use of zip codes to exclude them? In the following Parts, I provide a novel answer to that enduring puzzle. The answer, which I call the “Distinct Threads” approach, rests on the claim that though equal protection doctrine prohibits race-based differential treatment and prohibits actions taken with impermissible intent, these two proscriptions rest on different foundations and cannot be combined. In addition, I argue that the focus on impermissible intent is poorly understood. What the doctrine prohibits is only actions that are motivated by the harm they cause to individuals or groups. Facially neutral policies that are deliberately adopted in order to increase racial diversity neither involve race-based differential treatment, nor are they motivated by the desire to cause harm. As a result, these actions are permissible.

The argument for this solution proceeds as follows. Part I lays out the question this Article investigates and describes the three conceptually available answers. In addition, Part I describes how other scholars have analyzed the question and explains why their answers are unsatisfactory. Part II describes my proposed solution. It argues for the claim that the prohibitions contained within equal protection doctrine cannot be combined and diagnoses why the prohibition on invidious intent has been misunderstood. Part III elaborates this account, explaining why harm must be assessed objectively and describing how current doctrine supports the view that racial isolation is a harm. In addition, Part III discusses the implications of this account for our understanding of the term “race conscious” and for how courts ought to evaluate implicit bias. A brief conclusion follows.

  1.  Transcript of Oral Argument at 13–14, Students for Fair Admissions, Inc. v. President & Fellows of Harvard Coll., 143 S. Ct. 2141 (2023) (No. 21-707). ↑
  2.  143 S. Ct. 2141 (2023). ↑
  3.  The use of race in admissions was invalidated because the interests allegedly served by student body diversity are too difficult to measure and because the means adopted to achieve these ends are inapt. Id. at 2166–67. Additionally, in the Court’s view, the policies at issue rely on racial stereotypes and harm those not preferred by the policies. Id. at 2168–70. Chief Justice Roberts’s opinion for the Court does not, however, explicitly overrule Grutter v. Bollinger, 539 U.S. 306 (2003), and explicitly leaves open the question of whether the consideration of race is also impermissible in the context of admissions at the military academies. SFFA, 143 S. Ct. at 2166 n.4 (“This opinion also does not address the issue, in light of the potentially distinct interests that military academies may present.”). ↑
  4.  The term “facially neutral” refers to a law or policy that does not explicitly classify on protected grounds. ↑
  5.  The Court in SFFA considered both a constitutional claim and a statutory claim but viewed the prohibition on race discrimination contained in Title VI of the Civil Rights Act of 1964 as equivalent to the constitutional requirements of equal protection. For this reason, the result of the constitutional analysis will also have implications for non-state actors. See SFFA, 143 S. Ct. at 2156 n.2 (explaining that the Court would “evaluate Harvard’s admissions program under the standards of the Equal Protection Clause” because “discrimination that violates the Equal Protection Clause of the Fourteenth Amendment committed by an institution that accepts federal funds also constitutes a violation of Title VI” (quoting Gratz v. Bollinger, 539 U.S. 244, 276 n.23 (2003))). ↑
  6.  See Transcript of Oral Argument at 13–14, SFFA, 143 S. Ct. 2141 (No. 21-707). ↑
  7.  See id. at 13, 43–45. ↑
  8.  Id. at 13. Mr. Strawbridge seems to equivocate in his answer to Justice Kagan’s question. He initially seems to say that such policies would be permissible but ultimately does say that if the university adopted the policy at least in part because it would increase diversity and would not have adopted it without this reason, then the facially neutral policy would be impermissible. Id. at 16 (“[I]f the only reason to do it is through the narrow lens of race and there is no other race-neutral justification for it that the government can come forward and demonstrate that would have led it to adopt that policy anyway, I think . . . that’s the only scenario where it would create problems under the Court’s precedent.”). ↑
  9.  See, e.g., Wygant v. Jackson Bd. of Educ., 476 U.S. 267, 280 n.6 (1986); Grutter v. Bollinger, 539 U.S. 306, 340 (2003) (“We are satisfied that the Law School adequately considered race-neutral alternatives currently capable of producing a critical mass without forcing the Law School to abandon the academic selectivity that is the cornerstone of its educational mission.”); id. at 342 (“Universities in other States can and should draw on the most promising aspects of these race-neutral alternatives as they develop.”). ↑
  10.  See, e.g., Fisher v. Univ. of Tex. at Austin (Fisher II), 579 U.S. 365, 426–27 (2016) (Alito, J., dissenting) (noting that if the University of Texas at Austin adopted race-neutral policies it could achieve diversity “without injecting race into the process”); Grutter, 539 U.S. at 394–95 (Kennedy, J., dissenting) (“Were the courts to apply a searching standard to race-based admissions schemes, that would force educational institutions to seriously explore race-neutral alternatives. . . . Other programs do exist which will be more effective in bringing about the harmony and mutual respect among all citizens that our constitutional tradition has always sought. They, and not the program under review here, should be the model, even if the Court defaults by not demanding it.”); Gratz, 539 U.S. at 297 (Souter, J., dissenting) (“Drawing on admissions systems used at public universities in California, Florida, and Texas, the United States contends that Michigan could get student diversity in satisfaction of its compelling interest by guaranteeing admission to a fixed percentage of the top students from each high school in Michigan. . . . [T]here is nothing unconstitutional about such a practice . . . .”). ↑
  11.  Judge Heytens makes precisely this argument in response to the claim that the deliberate use of race-neutral polices to increase racial diversity transforms the policy into one that is race-based. See Coal. for TJ v. Fairfax Cnty. Sch. Bd., 68 F.4th 864, 891 (4th Cir. 2023) (Heytens, J., concurring) (emphasizing that “it would be quite the judicial bait-and-switch to say such race-neutral efforts are also presumptively unconstitutional” after spending “decades telling school officials they must consider race-neutral methods for ensuring a diverse student body before turning to race-conscious ones”), cert. denied, No. 23-170, 2024 WL 674659 (U.S. Feb. 20, 2024). ↑
  12.  See, e.g., Brian T. Fitzpatrick, Can Michigan Universities Use Proxies for Race After the Ban on Racial Preferences?, 13 Mich. J. Race
    & L

    . 277, 283 (2007) (arguing that “antidiscrimination laws have been [and should be] interpreted to prohibit not only facial classifications, but also neutral classifications that were adopted to serve as proxies for the facially-prohibited one”); Richard A. Primus, Equal Protection and Disparate Impact: Round Three, 117 Harv. L. Rev. 493, 496 (2003) (exploring the possibility that disparate impact liability mandated by Title VII may violate the constitutional guarantee of equal protection because that statute was adopted with the purpose of redressing racial inequality); Kim Forde-Mazrui, The Constitutional Implications of Race-Neutral Affirmative Action, 88 Geo. L.J. 2331, 2364–65, 2377 (2000) (arguing that race-neutral affirmative action may be unconstitutional if motivated by the same purposes as affirmative action that relies on racial classification). See generally Larry Alexander & Kevin Cole, Discrimination by Proxy, 14 Const. Comment. 453 (1997) (assuming that the deliberate use of a facially neutral trait to select for people with a protected trait violates the anti-discrimination principle and arguing that equal protection doctrine is internally conflicted). ↑

  13.  Pers. Adm’r of Mass. v. Feeney, 442 U.S. 256, 279 (1979) (emphasis added) (requiring a showing that a defendant discriminated “‘because of,’ not merely ‘in spite of,’ its adverse effects upon an identifiable group” in order to impose liability). The Feeney Court upheld Massachusetts’s preference for veterans in civil service positions, despite the fact that the policy disproportionately excluded women, because the policy was not adopted in order to exclude women. Id. at 275. The clear implication of this holding is that had the policy been adopted in order to exclude women, it would be impermissible. ↑
  14.  The Chief Justice emphasizes this fact in SFFA. See SFFA, 143 S. Ct. 2141, 2175 (2023) (critiquing the dissent by asserting that “[w]hile the dissent would certainly not permit university programs that discriminated against black and Latino applicants, it is perfectly willing to let the programs here continue”). ↑
  15.  See id. at 2166; Regents of the Univ. of Cal. v. Bakke, 438 U.S. 265, 298–99 (1978) (holding that whether a racial classification is used for benign purposes or invidious ones is not relevant and instead that classifications that “touch upon an individual’s race or ethnic background” all require the application of strict scrutiny). ↑
  16.  SFFA, 143 S. Ct. at 2169 (“A benefit provided to some applicants but not to others necessarily advantages the former group at the expense of the latter.”). ↑
  17.  In another case, the Chief Justice has suggested that he would find such motivation suspect. See, for example, Chief Justice Roberts’s comment in Parents Involved, where he opined that “[r]acial balancing is not transformed from ‘patently unconstitutional’ to a compelling state interest simply by relabeling it ‘racial diversity,’” which suggests that he might find intending to select for people on the basis of race to also be a patently unconstitutional motivation. Parents Involved in Cmty. Schs. v. Seattle Sch. Dist. No. 1, 551 U.S. 701, 732 (2007). ↑
  18.  SFFA, 143 S. Ct. at 2225 (Kavanaugh, J., concurring) (quoting City of Richmond v. J.A. Croson Co., 488 U.S. 469, 526 (1989) (Scalia, J., concurring)). ↑
  19.  Id. at 2215 (Gorsuch, J., concurring). ↑
  20.  Id. at 2206 (Thomas, J., concurring). ↑
  21.  What Justice Thomas would say about such a case is especially uncertain, as other parts of his concurring opinion suggest that he finds questionable the very idea that racial diversity is important in higher education. See id. at 2207. ↑
  22.  For a recent analysis, see Sonja Starr, The Magnet School Wars and the Future of Colorblindness, 76 Stan. L. Rev.
    161, 163–64 (2024).

    ↑

  23.  See Coal. for TJ v. Fairfax Cnty. Sch. Bd., 68 F.4th 864, 871–72, 875–76 (4th Cir. 2023). Judge Rushing, dissenting, argued that a “school board’s motivation to racially balance its schools, even using the means of a facially neutral policy, must be tested under exacting judicial scrutiny.” Id. at 893 (Rushing, J., dissenting). ↑
  24.  See Petition for Writ of Certiorari at 13, Coal. for TJ, No. 23-170 (U.S. Aug. 21, 2023) (styling their first reason for granting the petition in these terms: “The Use of Facially Race-Neutral Admissions Criteria to Achieve Racial Balance Presents an Unsettled Question of National Importance”). ↑
  25.  The majority opinion in Coalition for TJ does not specifically address this issue. See generally Coal. for TJ, 68 F.4th 864. In Christa McAuliffe Intermediate School PTO, Inc. v. de Blasio, the plaintiffs unsuccessfully argued that “where the government shifts to a policy that treats applicants differently based on a factor that is designed to operate as a proxy for race, it becomes suspect.” 627 F. Supp. 3d 253, 263–65 (S.D.N.Y. 2022). ↑
  26.  See, for example, Lewis v. Ascension Parish School Board, 662 F.3d 343, 352 (5th Cir. 2011) (per curiam), in which the Fifth Circuit reversed the lower court’s summary judgment and remanded the case in light of the existence of genuine issues of material fact regarding whether a racially discriminatory purpose and effect were present. Then-Chief Judge Jones, concurring, asserted that geographic boundaries used in student assignment are not racially neutral if adopted for the purpose of achieving racial balance in the schools. Id. at 354 (Jones, C.J., concurring) (“Streets . . . may well be racial proxies because the district or its agents apparently knew and used the racial composition of the people living on those streets to pursue racial balancing.”). Following remand to the district court, the Fifth Circuit upheld the finding of no constitutional violation without reaching the question of whether discriminatory intent was present. Lewis v. Ascension Parish Sch. Bd., 806 F.3d 344, 358, 363 (5th Cir. 2015). ↑
  27.  Coal. for TJ v. Fairfax Cnty. Sch. Bd., No. 23-170, 2024 WL 674659, at *1 (U.S. Feb. 20, 2024). Justice Alito, joined by Justice Thomas, dissented from the denial of certiorari but did so for reasons unrelated to the question regarding what intentions are prohibited under equal protection doctrine. Id. at *1, 5 (Alito, J., dissenting from the denial of certiorari). ↑
  28.  See, e.g., Zeina Mohammed, Black Women Are More Likely to Die During Pregnancy. A Local Doula Program Aims to Change That, Bos. Globe (Dec. 30, 2022, 5:14 PM), https://www.bostonglobe.com/2022/12/30/metro/mass-general-brighams-doula-program-aim‌s-close-racial-disparities-childbirth/
    ;

    Working

    Together to Reduce Black Maternal Mortality, Ctrs. for Disease Control & Prevention, https://www.cdc.gov/womens-health/features/matern‌al-mortality.html [https://perma.cc/4TR9-VV7E] (last updated Apr. 8, 2024). ↑

  29.  Where a constitutionally impermissible reason is a motivating reason for facially neutral state action, the burden shifts to the state to show that the same decision would have been reached absent the impermissible motivation. If the state is unable to do so, the facially neutral policy is subject to heightened review. See Village of Arlington Heights v. Metro. Hous. Dev. Corp., 429 U.S. 252, 265–66, 270 n.21 (1977). ↑

  30.  See supra note 5. ↑

The Zero-Sum Argument, Legacy Preferences, and the Erosion of the Distinction Between Disparate Treatment and Disparate Impact

In a complaint recently filed with the Department of Education,1 1.Complaint Under Title VI of the Civil Rights Act of 1964 at 3, Chica Project, Afr. Cmty. Econ. Dev. of New Eng. & Greater Bos. Latino Network v. President & Fellows of Harvard Coll., No. 01-23-2231 (Off. of C.R., U.S. Dep’t of Educ. July 3, 2023) [hereinafter Complaint].Show More a group of civil rights organizations allege that Harvard University’s legacy preference unlawfully discriminates against minority applicants in violation of Title VI of the Civil Rights Act of 1964.2 2.The organizations include Chica Project, African Community Economic Development of New England, and Greater Boston Latino Network.Show More In response, the Department of Education has opened an inquiry.3 3.Letter from Ramzi Ajami, Regional Director, Off. of C.R., U.S. Dep’t of Educ., to Michael A. Kippins, Laws. for C.R. (July 24, 2023), http://lawyersforcivilrights.org/wp-content/‌uploa‌ds/2023/07/Harvard-Complaint-Case-01-23-2231.pdf [https://perma.cc/7J4V-ENKF].Show More Interestingly, the Complainants deploy the argument made by Chief Justice Roberts in Students for Fair Admissions, Inc. v. President & Fellows of Harvard College (SFFA) that “[c]ollege admissions are zero-sum,” and so, a “benefit provided to some applicants but not to others necessarily advantages the former group at the expense of the latter.”4 4.Students for Fair Admissions, Inc. v. President & Fellows of Harvard Coll., 143 S. Ct. 2141, 2152 (2023).Show More Using this argument, the complaint alleges that a legacy preference cannot simply be viewed as a benefit to the relatives of alumni; it must simultaneously be viewed as a detriment to applicants who have no relation to alumni, a group we might call “non-legacies.”5 5.Complaint, supra note 1, at 3.Show More Because minority applicants are disproportionately represented among the non-legacy group, the legacy preference has a disparate impact on minority applicants.6 6.Peter Arcidiacono, Josh Kinsler & Tyler Ransom, Legacy and Athlete Preferences at Harvard, 40 J. Lab. Econ. 133, 135 (2022) (modeling the effect of removing admissions preferences at Harvard for legacies and athletes and concluding that the racial composition of the class would be significantly different (and less white) without them).Show More The complaint goes on to argue that the preference for legacies has no educational benefit, making this disparate impact unlawful.7 7.Complaint, supra note 1, at 24 (emphasizing that “[i]n light of the most recent pronouncement from the Supreme Court, it is difficult to see how fostering ‘a vital sense of engagement and support’—one of Harvard’s stated goals for Donor and Legacy Preferences—could qualify as an educational necessity sufficient to justify disproportionate impact under Title VI”).Show More

I am not sure that Complainants need the zero-sum argument to state a claim for disparate impact, but it certainly strengthens their argument, both logically and rhetorically. What I want to explore is whether Complainants could have done even more with the zero-sum argument. In particular, I am interested in exploring whether the zero-sum argument implicitly erodes the firm doctrinal distinction between disparate treatment and disparate impact, or, at the least, exposes an important conceptual linkage between the two forms of discrimination.

In SFFA, Chief Justice Roberts asserts that under current doctrine race can never be a “negative.”8 8.Students for Fair Admissions, 143 S. Ct. at 2175.Show More In his view, “our cases have stressed that an individual’s race may never be used against him in the admissions process.”9 9.Id. at 2168.Show More None of the other Justices or litigants take issue with that assertion. Rather, Harvard College and the University of North Carolina (“UNC”) claim that their admissions policies do not make race a negative; it is a plus for some applicants in some contexts but never a minus.10 10.Brief in Opposition at 22, Students for Fair Admissions, Inc. v. President & Fellows of Harvard Coll., 143 S. Ct. 2141 (2023) (No. 20-1199); Brief in Opposition by University Respondents at 7, Students for Fair Admissions, Inc. v. Univ. of N.C., 143 S. Ct. 2141 (2023) (No. 21-707).Show More Chief Justice Roberts finds this argument “hard to take seriously” because university admissions are “zero-sum.”11 11.Students for Fair Admissions, 143 S. Ct. at 2169.Show More In his view, a plus in the admissions process given to Black and Latinx students, for example, is a minus to white students and others not eligible for this benefit. To put the claim in a formal fashion, we might restate it as follows: in contexts like admissions, where the number of positive outcomes is limited, considering Trait X as a plus for Applicant A necessarily requires the decision-maker to treat the lack of Trait X as a minus for Applicant B. Let’s call this the Zero-Sum Claim.

In what follows, I examine the Zero-Sum Claim in the context of the recently challenged legacy preference and explore the implications of its underlying logic for the doctrinal distinction in U.S. anti-discrimination law between disparate treatment and disparate impact.

The first part of what the Zero-Sum Claim asserts is that if Harvard affords a preference to members of some minority groups, it necessarily advantages those applicants at the expense of applicants who are not members of these groups. The validity of this point was disputed by the Justices who dissented in SFFA.12 12.See id. at 2249 (Sotomayor, J., dissenting).Show More In their view, while only some applicants could garner a plus for minority race, all applicants were able to garner plusses for the various forms of diversity that each applicant was able to bring, and so non-minority students were not disadvantaged.13 13.Id.Show More In addition, all students benefit from the educational benefits of a diverse student body, so no one is disadvantaged.14 14.Id.Show More Whether this part of the Zero-Sum Claim holds up, I leave for another day. This Essay proceeds on the assumption that Chief Justice Roberts has the better argument on this point, and that if a college affords a preference to people with Trait X, it advantages people with X at the expense of people without X.

One might think that this is all there is to the Zero-Sum Claim and that the important argument is the one I’ve just put to the side. But, while it is easy to miss, the Zero-Sum Claim actually goes a step further. Chief Justice Roberts not only claims that the groups not benefited are at a competitive disadvantage, he also asserts that the race of those applicants is treated as a negative in the admissions processes at Harvard and UNC. In other words, this competitive disadvantage is the equivalent of giving these non-minority candidates a minus.15 15.Id. at 2169 (majority opinion).Show More

How could this be so? After all, no one asserts that Harvard actually subtracts points from the point tally of these applicants. Rather, people without X are at a disadvantage, and are burdened by the preference, because they are ineligible for points that others can accumulate. If admissions spots are scarce and competition for them is fierce (as is the case with respect to admissions at elite institutions like Harvard and UNC), then if two students are similar in other respects but one is an underrepresented minority and the other is not, the one who is an underrepresented minority will have more points. If the number of points determine who is admitted (and let’s assume that is the case), then between two otherwise similar students, non-minority status functions as a negative for that candidate.

This argument works by drawing attention to the effect of the racial preference. The preference does not itself constitute an aversion for non-minority candidates. Rather, the preferences are effectively, functionally, a detriment to applicants who are non-minority because of the competitive nature of college admissions. But here’s the rub. Current doctrine draws a firm distinction between policies that explicitly treat people differently on the basis of some trait (disparate treatment) and those that have that effect (disparate impact). A racial preference provides a plus to candidates of particular races. It does not formally or explicitly provide a minus to non-minority applicants. Rather, it has that effect. Similarly, Harvard’s legacy preference provides a benefit to applicants who are legacies. It did not formally, explicitly provide a minus to applicants who are not legacies. Rather, it has that effect.

The Chief Justice’s Zero-Sum Claim rests, albeit inadvertently, on the assumption that the effects of a policy matter to whether the policy treats the race of an applicant as a negative. In so doing, the argument erodes the distinction between disparate treatment and disparate impact. This feature of the Zero-Sum Claim is important. While the logic of the Claim does not dissolve the distinction between disparate treatment and disparate impact, the fact that the effect of a benefit transforms that benefit into a “negative” takes a meaningful step toward softening the distinction between these two forms of discrimination that are embedded in current doctrine.

A few caveats are in order, however, that lessen the force of the argument I have just offered. First, the Zero-Sum Claim applies only to contexts that could be described as zero-sum, that is, to situations of scarcity in which people are directly competing against each other for limited resources. Disparate treatment can occur in situations that do not have this structure and so the argument would not be relevant in these other contexts.

Second, the Chief Justice does not need the Zero-Sum Claim to find Harvard’s admissions policy involves disparate treatment on the basis of race. The fact that members of some races get a plus is sufficient for the policy to constitute disparate treatment on the basis of race. Nonetheless, the opinion contains the further assertion that race can never be used as a negative.16 16.Id. at 2175.Show More It is unclear what work this addition does, as the admissions policies have other constitutional flaws in the Court’s view, including that they impermissibly stereotype,17 17.Id. at 2169–70.Show More lack a clear end point,18 18.Id. at 2170–72.Show More and that the interests that allegedly justify the use of race are defined too amorphously to satisfy strict scrutiny.19 19.Id. at 2166.Show More Given all these other problems with the admissions policies at issue, the argument that rests on the Zero-Sum Claim is potentially superfluous.20 20.One might wonder why the Court needs to stress that race may never be used as a negative. Given that the opinion does not explicitly overrule Grutter v. Bollinger, 539 U.S. 306 (2003), it does not say that diversity is not a compelling interest, nor that narrow tailoring can never be achieved. Instead, the Court finds that the use of race in the admissions processes of Harvard and UNC do not satisfy Grutter. Part of the reason they fail is that race is used as a negative. This argument thus leaves open whether the use of race as a positive is still permissible in contexts that are not zero-sum and thus in which a positive for some is not automatically transformed into a negative for others. See Students for Fair Admissions, 143 S. Ct. at 2165–75.Show More

Third, the Zero-Sum Claim asserts that a benefit to some races is effectively a negative for members of other races. This form differs from the standard disparate impact claim in which a differentiation on facially neutral grounds (test scores, a legacy preference, etc.) is alleged to have a disparate impact on a group defined by a protected trait (race, for example). To say that a benefit for people with X is a detriment for people without X is not the same as saying that a benefit for people with X is a detriment for people with Y. Because disparate impact claims have this latter form, one more step is needed to fully dismantle the distinction between disparate treatment and disparate impact, which is likely why the Complainants challenging Harvard’s legacy preference made only a disparate impact claim and not, at the same time, a disparate treatment claim.

So, the modest first claim I am making is this: the fact that a benefit to some people becomes a negative to others because of its effect in a zero-sum context lessens the clarity of the distinction between disparate treatment and disparate impact. Of this modest claim, I am quite confident. At the same time, I wonder whether it is possible to advance a stronger argument: that Complainants challenging Harvard’s legacy preference might have alleged that this policy makes race — specifically, the races of non-white students — a negative.

Let’s try out that argument.

  1. The legacy preference provides a benefit for legacies.
  2. In a zero-sum context, a benefit to people with X becomes a detriment to people without X if the benefit has that effect. [The Zero-Sum Claim]
  3. Thus, a benefit to legacies is a detriment to non-legacies in the Harvard application process. [Modest Conclusion]
  4. Legacies are predominantly white.
  5. Thus, the legacy preference not only has the effect of disadvantaging applicants who are non-legacies, it also functionally disadvantages non-white applicants.
  6. Therefore, the legacy preference constitutes not only a preference for legacies but also, at the same time, a negative for both non-legacies and non-whites. [Strong Conclusion]

Step six dismantles the distinction between disparate treatment and disparate impact.

Chief Justice Roberts might respond to this argument by disputing that steps 1–5 lead to the conclusion in step 6. To do so, he might point out that a legacy preference will functionally disadvantage all non-legacies, but it does not disadvantage all non-white applicants (as some non-white applicants are also legacies). And so, the legacy preference does count as a minus for non-legacies but not as a minus for non-white applicants.

Is this rebuttal effective?

It certainly describes a feature that distinguishes the two cases. But merely pointing out a difference does not tell us that the difference matters. One could hardly explain to two plaintiffs with similar cases that one won and the other lost because the former was wearing a blue shirt and the latter was not. So, the question we must consider is whether the difference this rebuttal refers to is a relevant difference. Does it matter that all non-legacies will be burdened by the legacy preference and only some, most, or nearly all non-white applicants will be burdened by it?

The answer to this question depends on how strongly to take the implicit premise of the Zero-Sum Claim. When Chief Justice Roberts explains why the race-based preference for minority applicants is a negative for those who are not members of the racial groups preferred, he explains his reasoning as follows: “How else but ‘negative’ can race be described if, in its absence, members of some racial groups would be admitted in greater numbers than they otherwise would have been?”21 21.Id. at 2169.Show More According to this rationale, the progression to step 6 is easily defensible. The legacy preference functionally disadvantages non-legacies because, in its absence, non-legacies would be admitted in greater numbers than they otherwise would have been. Check. Now, let’s try it for racial minorities. The legacy preference functionally disadvantages non-white applicants because in its absence, members of this group (non-whites) would be admitted in greater numbers.22 22.Arcidiacono et al., supra note 6, at 153 (modeling the effect of abandoning legacy, athletic, and other preferences in the admissions process and determining that without legacy preferences, the percentage of underrepresented minorities admitted would increase and the percentage of white students admitted would decrease).Show More Again, check.23 23.See Students for Fair Admissions, 143 S. Ct. at 2169. This is precisely the argument Chief Justice Roberts offers in SFFA concluding that race is a negative in the admissions processes at issue, because “respondents also maintain that the demographics of their admitted classes would meaningfully change if race-based admissions were abandoned.” Id.Show More

If the reason that the racial preference in SFFA makes race a negative for some applicants is that in “its absence, members of some racial groups would be admitted in greater numbers than they otherwise would have been,” then the legacy preference at Harvard also makes race a negative for some applicants because in the absence of the legacy preference, members of some racial groups would have been admitted in greater numbers than they otherwise would have been.24 24.Id.Show More

At this point, I expect that some readers are still skeptical. Perhaps I have not stated the objection as forcefully as I might. Consider this version of the objection, one that insists that I am stretching the Zero-Sum Claim beyond where it will go. The benefit to legacies is necessarily a detriment to non-legacies. However, the benefit to legacies is only contingently a detriment to non-white applicants. This difference between the two cases might be thought especially important because if the connection is a necessary one, then perhaps I am not entitled to say that it is the effect of the preference that makes the benefit equivalent to a negative. If this objection is a good one, it challenges my assertion that the Zero-Sum Claim erodes the disparate treatment / disparate impact distinction.

This challenge is also unsuccessful, however. It is true that the relationship between legacies and non-legacies is reciprocal (everyone is either a legacy or a non-legacy) and so a benefit to a legacy is simply a lack of benefit to a non-legacy. But to make the jump from an absence of benefit to a negative, which is after all what the Chief Justice asserts in the Zero-Sum Claim, the Court must look outside of the necessary truth that “X” and “not X” stand in a necessary relationship to each other. He must refer to the fact that admissions at Harvard and UNC are competitive and admissions spots are scarce. It is these contingent facts about university admissions at Harvard and UNC that makes the racial preference a negative for those not preferred.

As a result, the fact that a legacy preference is also a “negative” to non-legacies is not actually necessary; it is a contingent fact that depends on the competitive environment at the schools. But once this contingency is conceded, the implications of the argument widen. In the competitive zero-sum environment of admissions, a legacy preference also makes race a negative for students of color seeking acceptance to competitive schools like Harvard.

One might wonder about the implications of the argument just offered. If the Zero-Sum Claim erodes the distinction between disparate treatment and disparate impact, then courts will need to determine how both should be treated. They could decide that disparate impact claims will be treated like disparate treatment claims (leveling up), or they could instead decide that disparate treatment claims will be treated like disparate impact claims (leveling down). Either is possible. The point of this piece is conceptual, rather than normative, and so it does not provide reasons to favor one approach over the other. That said, I welcome the implicit recognition that the Zero-Sum Claim provides for a view that disparate treatment and disparate impact are often different in degree rather than in kind and normatively less different than constitutional doctrine currently acknowledges.

  1.  Complaint Under Title VI of the Civil Rights Act of 1964 at 3, Chica Project, Afr. Cmty. Econ. Dev. of New Eng. & Greater Bos. Latino Network v. President & Fellows of Harvard Coll., No. 01-23-2231 (Off. of C.R., U.S. Dep’t of Educ. July 3, 2023) [hereinafter Complaint]. ↑
  2.  The organizations include Chica Project, African Community Economic Development of New England, and Greater Boston Latino Network. ↑
  3.  Letter from Ramzi Ajami, Regional Director, Off. of C.R., U.S. Dep’t of Educ., to Michael A. Kippins, Laws. for C.R. (July 24, 2023), http://lawyersforcivilrights.org/wp-content/‌uploa‌ds/2023/07/Harvard-Complaint-Case-01-23-2231.pdf [https://perma.cc/7J4V-ENKF]. ↑
  4.  Students for Fair Admissions, Inc. v. President & Fellows of Harvard Coll., 143 S. Ct. 2141, 2152 (2023). ↑
  5.  Complaint, supra note 1, at 3. ↑
  6.  Peter Arcidiacono, Josh Kinsler & Tyler Ransom, Legacy and Athlete Preferences at Harvard, 40 J. Lab. Econ. 133, 135 (2022) (modeling the effect of removing admissions preferences at Harvard for legacies and athletes and concluding that the racial composition of the class would be significantly different (and less white) without them). ↑
  7.  Complaint, supra note 1, at 24 (emphasizing that “[i]n light of the most recent pronouncement from the Supreme Court, it is difficult to see how fostering ‘a vital sense of engagement and support’—one of Harvard’s stated goals for Donor and Legacy Preferences—could qualify as an educational necessity sufficient to justify disproportionate impact under Title VI”). ↑
  8.  Students for Fair Admissions, 143 S. Ct. at 2175. ↑
  9.  Id. at 2168. ↑
  10.  Brief in Opposition at 22, Students for Fair Admissions, Inc. v. President & Fellows of Harvard Coll., 143 S. Ct. 2141 (2023) (No. 20-1199); Brief in Opposition by University Respondents at 7, Students for Fair Admissions, Inc. v. Univ. of N.C., 143 S. Ct. 2141 (2023) (No. 21-707). ↑
  11.  Students for Fair Admissions, 143 S. Ct. at 2169. ↑
  12.  See id. at 2249 (Sotomayor, J., dissenting). ↑
  13.  Id. ↑
  14.  Id. ↑
  15.  Id. at 2169 (majority opinion). ↑
  16.  Id. at 2175. ↑
  17.  Id. at 2169–70. ↑
  18.  Id. at 2170–72. ↑
  19.  Id. at 2166. ↑
  20.  One might wonder why the Court needs to stress that race may never be used as a negative. Given that the opinion does not explicitly overrule Grutter v. Bollinger, 539 U.S. 306 (2003), it does not say that diversity is not a compelling interest, nor that narrow tailoring can never be achieved. Instead, the Court finds that the use of race in the admissions processes of Harvard and UNC do not satisfy Grutter. Part of the reason they fail is that race is used as a negative. This argument thus leaves open whether the use of race as a positive is still permissible in contexts that are not zero-sum and thus in which a positive for some is not automatically transformed into a negative for others. See Students for Fair Admissions, 143 S. Ct. at 2165–75. ↑
  21.  Id. at 2169. ↑
  22.  Arcidiacono et al., supra note 6, at 153 (modeling the effect of abandoning legacy, athletic, and other preferences in the admissions process and determining that without legacy preferences, the percentage of underrepresented minorities admitted would increase and the percentage of white students admitted would decrease). ↑
  23.  See Students for Fair Admissions, 143 S. Ct. at 2169. This is precisely the argument Chief Justice Roberts offers in SFFA concluding that race is a negative in the admissions processes at issue, because “respondents also maintain that the demographics of their admitted classes would meaningfully change if race-based admissions were abandoned.” Id. ↑

  24.  Id. ↑

Measuring Algorithmic Fairness

Algorithmic decision making is both increasingly common and increasingly controversial. Critics worry that algorithmic tools are not transparent, accountable, or fair. Assessing the fairness of these tools has been especially fraught as it requires that we agree about what fairness is and what it requires. Unfortunately, we do not. The technological literature is now littered with a multitude of measures, each purporting to assess fairness along some dimension. Two types of measures stand out. According to one, algorithmic fairness requires that the score an algorithm produces should be equally accurate for members of legally protected groups—blacks and whites, for example. According to the other, algorithmic fairness requires that the algorithm produce the same percentage of false positives or false negatives for each of the groups at issue. Unfortunately, there is often no way to achieve parity in both these dimensions. This fact has led to a pressing question. Which type of measure should we prioritize and why?

This Article makes three contributions to the debate about how best to measure algorithmic fairness: one conceptual, one normative, and one legal. Equal predictive accuracy ensures that a score means the same thing for each group at issue. As such, it relates to what one ought to believe about a scored individual. Because questions of fairness usually relate to action, not belief, this measure is ill-suited as a measure of fairness. This is the Article’s conceptual contribution. Second, this Article argues that parity in the ratio of false positives to false negatives is a normatively significant measure. While a lack of parity in this dimension is not constitutive of unfairness, this measure provides important reasons to suspect that unfairness exists. This is the Article’s normative contribution. Interestingly, improving the accuracy of algorithms overall will lessen this unfairness. Unfortunately, a common assumption that anti-discrimination law prohibits the use of racial and other protected classifications in all contexts is inhibiting those who design algorithms from making them as fair and accurate as possible. This Article’s third contribution is to show that the law poses less of a barrier than many assume.

Introduction

At an event celebrating Martin Luther King, Jr. Day, Representative Alexandria Ocasio-Cortez (D-NY) expressed the concern, shared by many, that algorithmic decision making is biased. “Algorithms are still made by human beings, and those algorithms are still pegged to basic human assumptions,” she asserted. “They’re just automated. And if you don’t fix the bias, then you are automating the bias.”1 1.Blackout for Human Rights, MLK Now 2019, Riverside Church in the City of N.Y. (Jan. 21, 2019), https://www.trcnyc.org/mlknow2019/ [https://perma.cc/L45Q-SN9T] (interview with Rep. Ocasio-Cortez begins at approximately minute 16, and comments regarding algorithms begin at approximately minute 40); see also Danny Li, AOC Is Right: Algorithms Will Always Be Biased as Long as There’s Systemic Racism in This Country, Slate (Feb. 1, 2019, 3:47 PM), https://slate.com/news-and-politics/2019/02/aoc-algorithms-racist-bias.html [https://perma.cc/S97Z-UH2U] (quoting Ocasio-Cortez’s comments at the event in New York); Cat Zakrzewski, The Technology 202: Alexandria Ocasio-Cortez Is Using Her Social Media Clout To Tackle Bias in Algorithms, Wash. Post: PowerPost (Jan. 28, 2019), https://www.washingtonpost.com/news/powerpost/paloma/the-technology-202/2019/01/28 /the-technology-202-alexandria-ocasio-cortez-is-using-her-social-media-clout-to-tackle-bias-in-algorithms/5c4dfa9b1b326b29c37­78cdd/?utm_term=.541cd0827a23 [https://perma.cc/ LL4Y-FWDK] (discussing Ocasio-Cortez’s comments and reactions to them).Show More The audience inside the room applauded. Outside the room, the reaction was more mixed. “Socialist Rep. Alexandria Ocasio-Cortez . . . claims that algorithms, which are driven by math, are racist,” tweeted a writer for the Daily Wire.2 2.Ryan Saavedra (@RealSaavedra), Twitter (Jan. 22, 2019, 12:27 AM), https://twitter.com/RealSaavedra/status/1087627739861897216 [https://perma.cc/32DD-QK5S]. The coverage of Ocasio-Cortez’s comments is mixed. See, e.g., Zakrzewski, supra note 1 (describing conservatives’ criticism of and other media outlets’ and experts’ support of Ocasio-Cortez’s comments).Show More Math is just math, this commentator contends, and the idea that math can be unfair is crazy.

This controversy is just one of many to challenge the fairness of algorithmic decision making.3 3.See, e.g., Hiawatha Bray, The Software That Runs Our Lives Can Be Biased—But We Can Fix It, Bos. Globe, Dec. 22, 2017, at B9 (describing a New York City Council member’s proposal to audit the city government’s computer decision systems for bias); Drew Harwell, Amazon’s Facial-Recognition Software Has Fraught Accuracy Rate, Study Finds, Wash. Post, Jan. 26, 2019, at A14 (reporting on an M.I.T. Media Lab study that found that Amazon facial-recognition software is less accurate with regard to darker-skinned women than lighter-skinned men, and Amazon’s criticism of the study); Tracy Jan, Mortgage Algorithms Found To Have Racial Bias, Wash. Post, Nov. 15, 2018, at A21 (reporting on a University of California at Berkeley study that found that black and Latino home loan customers pay higher interest rates than white or Asian customers on loans processed online or in person); Tony Romm & Craig Timberg, Under Bipartisan Fire from Congress, CEO Insists Google Does Not Take Sides, Wash. Post, Dec. 12, 2018, at A16 (reporting on Congresspeople’s concerns regarding Google algorithms which were voiced at a House Judiciary Committee hearing with Google’s CEO).Show More The use of algorithms, and in particular their connection with machine learning and artificial intelligence, has attracted significant attention in the legal literature as well. The issues raised are varied, and include concerns about transparency,4 4.See, e.g., Danielle Keats Citron, Technological Due Process, 85 Wash. U. L. Rev. 1249, 1288–97 (2008); Natalie Ram, Innovating Criminal Justice, 112 Nw. U. L. Rev. 659 (2018); Rebecca Wexler, Life, Liberty, and Trade Secrets: Intellectual Property in the Criminal Justice System, 70 Stan. L. Rev. 1343 (2018).Show More accountability,5 5.See, e.g., Margot E. Kaminski, Binary Governance: Lessons from the GDPR’s Approach to Algorithmic Accountability, 92 S. Cal. L. Rev. 1529 (2019); Joshua A. Kroll et al., Accountable Algorithms, 165 U. Pa. L. Rev. 633 (2017); Anne L. Washington, How To Argue with an Algorithm: Lessons from the COMPAS-ProPublica Debate, 17 Colo. Tech. L.J. 131 (2018) (arguing for standards governing the information available about algorithms so that their accuracy and fairness can be properly assessed). But see Jon Kleinberg et al., Discrimination in the Age of Algorithms (Nat’l Bureau of Econ. Research, Working Paper No. 25548, 2019), http://www.nber.org/papers/w25548 [https://perma.cc/JU6H-HG3W] (analyzing the potential benefits of algorithms as tools to prove discrimination).Show More privacy,6 6.See generally Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information (2015) (discussing and critiquing internet and finance companies’ non-transparent use of data tracking and algorithms to influence and manage people); Anupam Chander, The Racist Algorithm?, 115 Mich. L. Rev. 1023, 1024 (2017) (reviewing Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information (2015)) (arguing that instead of “transparency in the design of the algorithm” that Pasquale argues for, “[w]hat we need . . . is a transparency of inputs and results”) (emphasis omitted).Show More and fairness.7 7.See, e.g., Aziz Z. Huq, Racial Equity in Algorithmic Criminal Justice, 68 Duke L.J. 1043 (2019) (arguing that current constitutional doctrine is ill-suited to the task of evaluating algorithmic fairness and that current standards offered in the technology literature miss important policy concerns); Sandra G. Mayson, Bias In, Bias Out, 128 Yale L.J. 2218 (2019) (discussing how past and existing racial inequalities in crime and arrests mean that methods to predict criminal risk based on existing information will result in racial inequality).Show More This Article focuses on fairness—the issue raised by Ocasio-Cortez. It focuses on how we should assess what makes algorithmic decision making fair. Fairness is a moral concept, and a contested one at that. As a result, we should expect that different people will offer well-reasoned arguments for different conceptions of fairness. And this is precisely what we find.

The computer science literature is filled with a proliferation of measures, each purporting to capture fairness along some dimension. This Article provides a pathway through that morass. It makes three contributions: one conceptual, one normative, and one legal. This Article argues that one of the dominant measures of fairness offered in the literature tells us what to believe, not what to do, and thus is ill-suited as a measure of fair treatment. This is the conceptual claim. Second, this Article argues that the ratio between false positives and false negatives offers an important indicator of whether members of two groups scored by an algorithm are treated fairly, vis-à-vis each other. This is the normative claim. Third, this Article challenges a common assumption that anti-discrimination law prohibits the use of racial and other protected classifications in all contexts. Because using race within algorithms can increase both their accuracy and fairness, this misunderstanding has important implications. This Article’s third contribution is to show that the law poses less of a barrier than many assume.

We can use the controversy over a common risk assessment tool used by many states for bail, sentencing, and parole to illustrate the controversy about how best to measure fairness.8 8.See Julia Angwin et al., Machine Bias, ProPublica (May 23, 2016), https://www.pro­publica.org/article/machine-bias-risk-assessments-in-criminal-sentencing [https://perma.cc/BA53-JT7V].Show More The tool, called COMPAS, assigns each person a score that indicates the likelihood that the person will commit a crime in the future.9 9.Equivant, Practitioner’s Guide to COMPAS Core 7 (2019), http://www.equivant.com/wp-content/uploads/Practitioners-Guide-to-COMPAS-Core-040419.pdf [https://perma.cc/LRY6-RXAH].Show More In a high-profile exposé, the website ProPublica claimed that COMPAS treated blacks and whites differently because black arrestees and inmates were far more likely to be erroneously classified as risky than were white arrestees and inmates despite the fact that COMPAS did not explicitly use race in its algorithm.10 10.See Angwin et al., supra note 8 (“Northpointe’s core product is a set of scores derived from 137 questions that are either answered by defendants or pulled from criminal records. Race is not one of the questions.”).Show More The essence of ProPublica’s claim was this:

In forecasting who would re-offend, the algorithm made mistakes with black and white defendants at roughly the same rate but in very different ways. The formula was particularly likely to falsely flag black defendants as future criminals, wrongly labeling them this way at almost twice the rate as white defendants. White defendants were mislabeled as low risk more often than black defendants.11 11.Id.Show More

Northpointe12 12.Northpointe, along with CourtView Justice Solutions Inc. and Constellation Justice Systems, rebranded to Equivant in January 2017. Equivant, Frequently Asked Questions 1, http://my.courtview.com/rs/322-KWH-233/images/Equivant%20Customer%20FAQ%20-%20FINAL.pdf [https://perma.cc/7HH8-LVQ6].Show More (the company that developed and owned COMPAS) responded to the criticism by arguing that ProPublica was focused on the wrong measure. In essence, Northpointe stressed the point ProPublica conceded—that COMPAS made mistakes with black and white defendants at roughly equal rates.13 13.See William Dieterich et al., COMPAS Risk Scales: Demonstrating Accuracy Equity and Predictive Parity, Northpointe 9–10 (July 8, 2016), http://go.volarisgroup.com/rs/430-MBX-989/images/ProPublica_Commentary_Final_070616.pdf [https://perma.cc/N5RL-M9RN].Show More Although Northpointe and others challenged some of the accuracy of ProPublica’s analysis,14 14.For a critique of ProPublica’s analysis, see Anthony W. Flores et al., False Positives, False Negatives, and False Analyses: A Rejoinder to “Machine Bias: There’s Software Used Across the Country To Predict Future Criminals. And It’s Biased Against Blacks.”, 80 Fed. Prob. 38 (2016).Show More the main thrust of Northpointe’s defense was that COMPAS does treat blacks and whites the same. The controversy focused on the manner in which such similarity is assessed. Northpointe focused on the fact that if a black person and a white person were each given a particular score, the two people would be equally likely to recidivate.15 15.See Dieterich et al., supra note 13, at 9–11.Show More ProPublica looked at the question from a different angle. Rather than asking whether a black person and a white person with the same score were equally likely to recidivate, it focused instead on whether a black and white person who did not go on to recidivate were equally likely to have received a low score from the algorithm.16 16.See Angwin et al., supra note 8 (“In forecasting who would re-offend, the algorithm made mistakes with black and white defendants at roughly the same rate but in very different ways.”).Show More In other words, one measure begins with the score and asks about its ability to predict reality. The other measure begins with reality and asks about its likelihood of being captured by the score.

The easiest way to fix the problem would be to treat the two groups equally in both respects. A high score and low score should mean the same thing for both blacks and whites (the measure Northpointe emphasized), and law-abiding blacks and whites should be equally likely to be mischaracterized by the tool (the measure ProPublica emphasized). Unfortunately, this solution has proven impossible to achieve. In a series of influential papers, computer scientists demonstrated that, in most circumstances, it is simply not possible to equalize both measures.17 17.See, e.g., Richard Berk et al., Fairness in Criminal Justice Risk Assessments: The State of the Art, Soc. Methods & Res. OnlineFirst 1, 23 (2018), https://journals.sagepub.com/doi/­10.1177/0049124118782533 [https://perma.cc/GG9L-9AEU] (discussing the required trade­off between predictive accuracy and various fairness measures); Alexandra Chouldechova, Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments, 5 Big Data 153, 157 (2017) (demonstrating that recidivism prediction instruments cannot simultaneously meet all fairness criteria where recidivism rates differ across groups because its error rates will be unbalanced across the groups when the instrument achieves predictive parity); Jon Kleinberg et al., Inherent Trade-Offs in the Fair Determination of Risk Scores, 67 LIPIcs 43:1, 43:5–8 (2017), https://drops.dagstuhl.de/opus/volltexte/2017/8156/pdf/LIPIcs-ITCS-2017-43.pdf [https://perma.cc/S9DM-PER2] (demonstrating how difficult it is for algorithms to simultaneously achieve the fairness goals of calibration and balance in predictions involving different groups).Show More The reason it is impossible relates to the fact that the underlying rates of recidivism among blacks and whites differ.18 18.See Bureau of Justice Statistics, U.S. Dep’t of Justice, 2018 Update on Prisoner Recidivism: A 9-Year Follow-up Period (2005–2014) 6 tbl.3 (2018), https://www.bjs.gov/­content/pub/pdf/18upr9yfup0514.pdf [https://perma.cc/3UE3-AS5S] (analyzing rearrests of state prisoners released in 2005 in 30 states and finding that 86.9% of black prisoners and 80.9% of white prisoners were arrested in the nine years following their release); see also Dieterich et al., supra note 13, at 6 (“[I]n comparison with blacks, whites have much lower base rates of general recidivism . . . .”). Of course, the data on recidivism itself may be flawed. This consideration is discussed below. See infra text accompanying notes 33–37.Show More When the two groups at issue (whatever they are) have different rates of the trait predicted by the algorithm, it is impossible to achieve parity between the groups in both dimensions.19 19.This is true unless the tool makes no mistakes at all. Kleinberg et al., supra note 17, at 43:5–6.Show More The example discussed in Part I illustrates this phenomenon.20 20.See infra Section I.A.Show More This fact gives rise to the question: in which dimension is such parity more important and why?

These different measures are often described as different conceptions of fairness.21 21.For example, Berk et al. consider six different measures of algorithmic fairness. See Berk et al., supra note 17, at 12–15.Show More This is a mistake. The measure favored by Northpointe is relevant to what we ought to believe about a particular scored individual. If a high-risk score means something different for blacks than for whites, then we do not know whether to believe (or how much confidence to have) in the claim that a particular scored individual is likely to commit a crime in the future. The measure favored by ProPublica relates instead to what we ought to do. If law-abiding blacks and law-abiding whites are not equally likely to be mischaracterized by the score, we will not know whether or how to use the scores in making decisions. If we are comparing a measure that is relevant to what we ought to believe to one that is relevant to what we ought to do, we are truly comparing apples to oranges.

This conclusion does not straightforwardly suggest that we should instead focus on the measure touted by ProPublica, however. A sophisticated understanding of the significance of these measures is fast-moving and evolving. Some computer scientists now argue that the lack of parity in the ProPublica measure is less meaningful than one might think.22 22.See Sam Corbett-Davies & Sharad Goel, The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning (arXiv, Working Paper No. 1808.00023v2, 2018), http://arxiv.org/abs/1808.00023 [https://perma.cc/ML4Y-EY6S].Show More The better way to understand the measure highlighted by ProPublica would be to say that it suggests that something is likely amiss. Differences in the ratio of false positive rates to false negative rates indicate that the algorithmic tool may rely on data that are themselves infected with bias or that the algorithm may be compounding a prior injustice. Because these possibilities have normative implications for how the algorithm should be used, this measure relates to fairness.

The most promising way to enhance algorithmic fairness is to improve the accuracy of the algorithm overall.23 23.See Sumegha Garg et al., Tracking and Improving Information in the Service of Fairness (arXiv, Working Paper No. 1904.09942v2, 2019), http://arxiv.org/abs/1904.09942 [https://perma.cc/D8ZN-CJ83].Show More And we can do that by permitting the use of protected traits (like race and sex) within the algorithm to determine what other traits will be used to predict the target variable (like recidivism). For example, housing instability might be more predictive of recidivism for whites than for blacks.24 24.See Sam Corbett-Davies et al., Algorithmic Decision Making and the Cost of Fairness, 2017 Proc. 23d ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining 797, 805.Show More If the algorithm includes a racial classification, it can segment its analysis such that this trait is used to predict recidivism for whites but not for blacks. Although this approach would improve risk assessment and thereby lessen the inequity highlighted by ProPublica, many in the field believe this approach is off the table because it is prohibited by law.25 25.See id. (“[E]xplicitly including race as an input feature raises legal and policy complications, and as such it is common to simply exclude features with differential predictive power.”).Show More This is not the case.

The use of racial classifications only sometimes constitutes disparate treatment on the basis of race and thus only sometimes gives rise to strict scrutiny. The fact that some uses of racial classifications do not constitute disparate treatment reveals that the concept of disparate treatment is more elusive than is often recognized. This observation is important given the central role that the distinction between disparate treatment and disparate impact plays in equal protection doctrine and statutory anti-discrimination law. In addition, it is important because it opens the door to more creative ways to improve algorithmic fairness.

The Article proceeds as follows. Part I develops the conceptual claim. It shows that the two most prominent types of measures used to assess algorithmic fairness are geared to different tasks. One is relevant to belief and the other to decision and action. This Part begins with a detailed explanation of the two measures and then explores the factors that affect belief and action in individual cases. Turning to the comparative context, Part I argues that predictive parity (the measure favored by Northpointe) is relevant to belief but not directly to the fair treatment of different groups.

Part II makes a normative claim. It argues that differences in the ratio of false positives to false negatives between protected groups (a variation on the measure put forward by ProPublica) suggest unfairness, and it explains why this is so. This Part begins by clarifying three distinct ways in which the concept of fairness is used in the literature. It then explains both the normative appeal of focusing on the parity in the ratio of false positives to false negatives and, at the same time, why doing so can be misleading. Despite these drawbacks, Part II argues that the disparity in the ratio of false positive to false negative rates tells us something important about the fairness of the algorithm.

Part III explores what can be done to diminish this unfairness. It argues that using protected classifications like race and sex within algorithms can improve their accuracy and fairness. Because constitutional anti­discrimination law generally disfavors racial classifications, computer scientists and others who work with algorithms are reluctant to deploy this approach. Part III argues that this reluctance rests on an overly simplistic view of the law. Focusing on constitutional law and on racial classification in particular, this Part argues that the doctrine’s resistance to the use of racial classifications is not categorical. Part III explores contexts in which the use of racial classifications does not constitute disparate treatment on the basis of race and extracts two principles from these examples. Using these principles, this Part argues that the use of protected classifications within algorithms may well be permissible. A conclusion follows.

  1. * D. Lurton Massee, Jr. Professor of Law and Roy L. and Rosamond Woodruff Morgan Professor of Law at the University of Virginia School of Law. I would like to thank Charles Barzun, Aloni Cohen, Aziz Huq, Kim Ferzan, Niko Kolodny, Sandy Mayson, Tom Nachbar, Richard Schragger, Andrew Selbst, and the participants in the Caltech 10th Workshop in Decisions, Games, and Logic: Ethics, Statistics, and Fair AI, the Dartmouth Law and Philosophy Workshop, and the computer science department at UVA for comments and critique. In addition, I would like to thank Kristin Glover of the University of Virginia Law Library and Judy Baho for their excellent research assistance. Any errors or confusions are my own. ↑
  2. Blackout for Human Rights, MLK Now 2019, Riverside Church in the City of N.Y. (Jan. 21, 2019), https://www.trcnyc.org/mlknow2019/ [https://perma.cc/L45Q-SN9T] (interview with Rep. Ocasio-Cortez begins at approximately minute 16, and comments regarding algorithms begin at approximately minute 40); see also Danny Li, AOC Is Right: Algorithms Will Always Be Biased as Long as There’s Systemic Racism in This Country, Slate (Feb. 1, 2019, 3:47 PM), https://slate.com/news-and-politics/2019/02/aoc-algorithms-racist-bias.html [https://perma.cc/S97Z-UH2U] (quoting Ocasio-Cortez’s comments at the event in New York); Cat Zakrzewski, The Technology 202: Alexandria Ocasio-Cortez Is Using Her Social Media Clout To Tackle Bias in Algorithms, Wash. Post: PowerPost (Jan. 28, 2019), https://www.washingtonpost.com/news/powerpost/paloma/the-technology-202/2019/01/28 /the-technology-202-alexandria-ocasio-cortez-is-using-her-social-media-clout-to-tackle-bias-in-algorithms/5c4dfa9b1b326b29c37­78cdd/?utm_term=.541cd0827a23 [https://perma.cc/ LL4Y-FWDK] (discussing Ocasio-Cortez’s comments and reactions to them). ↑
  3. Ryan Saavedra (@RealSaavedra), Twitter (Jan. 22, 2019, 12:27 AM), https://twitter.com/RealSaavedra/status/1087627739861897216 [https://perma.cc/32DD-QK5S]. The coverage of Ocasio-Cortez’s comments is mixed. See, e.g., Zakrzewski, supra note 1 (describing conservatives’ criticism of and other media outlets’ and experts’ support of Ocasio-Cortez’s comments). ↑
  4. See, e.g., Hiawatha Bray, The Software That Runs Our Lives Can Be Biased—But We Can Fix It, Bos. Globe, Dec. 22, 2017, at B9 (describing a New York City Council member’s proposal to audit the city government’s computer decision systems for bias); Drew Harwell, Amazon’s Facial-Recognition Software Has Fraught Accuracy Rate, Study Finds, Wash. Post, Jan. 26, 2019, at A14 (reporting on an M.I.T. Media Lab study that found that Amazon facial-recognition software is less accurate with regard to darker-skinned women than lighter-skinned men, and Amazon’s criticism of the study); Tracy Jan, Mortgage Algorithms Found To Have Racial Bias, Wash. Post, Nov. 15, 2018, at A21 (reporting on a University of California at Berkeley study that found that black and Latino home loan customers pay higher interest rates than white or Asian customers on loans processed online or in person); Tony Romm & Craig Timberg, Under Bipartisan Fire from Congress, CEO Insists Google Does Not Take Sides, Wash. Post, Dec. 12, 2018, at A16 (reporting on Congresspeople’s concerns regarding Google algorithms which were voiced at a House Judiciary Committee hearing with Google’s CEO). ↑
  5. See, e.g., Danielle Keats Citron, Technological Due Process, 85 Wash. U. L. Rev. 1249, 1288–97 (2008); Natalie Ram, Innovating Criminal Justice, 112 Nw. U. L. Rev. 659 (2018); Rebecca Wexler, Life, Liberty, and Trade Secrets: Intellectual Property in the Criminal Justice System, 70 Stan. L. Rev. 1343 (2018). ↑
  6. See, e.g., Margot E. Kaminski, Binary Governance: Lessons from the GDPR’s Approach to Algorithmic Accountability, 92 S. Cal. L. Rev. 1529 (2019); Joshua A. Kroll et al., Accountable Algorithms, 165 U. Pa. L. Rev. 633 (2017); Anne L. Washington, How To Argue with an Algorithm: Lessons from the COMPAS-ProPublica Debate, 17 Colo. Tech. L.J. 131 (2018) (arguing for standards governing the information available about algorithms so that their accuracy and fairness can be properly assessed). But see Jon Kleinberg et al., Discrimination in the Age of Algorithms (Nat’l Bureau of Econ. Research, Working Paper No. 25548, 2019), http://www.nber.org/papers/w25548 [https://perma.cc/JU6H-HG3W] (analyzing the potential benefits of algorithms as tools to prove discrimination). ↑
  7. See generally Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information (2015) (discussing and critiquing internet and finance companies’ non-transparent use of data tracking and algorithms to influence and manage people); Anupam Chander, The Racist Algorithm?, 115 Mich. L. Rev. 1023, 1024 (2017) (reviewing Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information (2015)) (arguing that instead of “transparency in the design of the algorithm” that Pasquale argues for, “[w]hat we need . . . is a transparency of inputs and results”) (emphasis omitted). ↑
  8. See, e.g., Aziz Z. Huq, Racial Equity in Algorithmic Criminal Justice, 68 Duke L.J. 1043 (2019) (arguing that current constitutional doctrine is ill-suited to the task of evaluating algorithmic fairness and that current standards offered in the technology literature miss important policy concerns); Sandra G. Mayson, Bias In, Bias Out, 128 Yale L.J. 2218 (2019) (discussing how past and existing racial inequalities in crime and arrests mean that methods to predict criminal risk based on existing information will result in racial inequality). ↑
  9. See Julia Angwin et al., Machine Bias, ProPublica (May 23, 2016), https://www.pro­publica.org/article/machine-bias-risk-assessments-in-criminal-sentencing [https://perma.cc/BA53-JT7V]. ↑
  10. Equivant, Practitioner’s Guide to COMPAS Core 7 (2019), http://www.equivant.com/wp-content/uploads/Practitioners-Guide-to-COMPAS-Core-040419.pdf [https://perma.cc/LRY6-RXAH]. ↑
  11. See Angwin et al., supra note 8 (“Northpointe’s core product is a set of scores derived from 137 questions that are either answered by defendants or pulled from criminal records. Race is not one of the questions.”). ↑
  12. Id. ↑
  13. Northpointe, along with CourtView Justice Solutions Inc. and Constellation Justice Systems, rebranded to Equivant in January 2017. Equivant, Frequently Asked Questions 1, http://my.courtview.com/rs/322-KWH-233/images/Equivant%20Customer%20FAQ%20-%20FINAL.pdf [https://perma.cc/7HH8-LVQ6]. ↑
  14. See William Dieterich et al., COMPAS Risk Scales: Demonstrating Accuracy Equity and Predictive Parity, Northpointe 9–10 (July 8, 2016), http://go.volarisgroup.com/rs/430-MBX-989/images/ProPublica_Commentary_Final_070616.pdf [https://perma.cc/N5RL-M9RN]. ↑
  15. For a critique of ProPublica’s analysis, see Anthony W. Flores et al., False Positives, False Negatives, and False Analyses: A Rejoinder to “Machine Bias: There’s Software Used Across the Country To Predict Future Criminals. And It’s Biased Against Blacks.”, 80 Fed. Prob. 38 (2016). ↑
  16. See Dieterich et al., supra note 13, at 9–11. ↑
  17. See Angwin et al., supra note 8 (“In forecasting who would re-offend, the algorithm made mistakes with black and white defendants at roughly the same rate but in very different ways.”). ↑
  18. See, e.g., Richard Berk et al., Fairness in Criminal Justice Risk Assessments: The State of the Art, Soc. Methods & Res. OnlineFirst 1, 23 (2018), https://journals.sagepub.com/doi/­10.1177/0049124118782533 [https://perma.cc/GG9L-9AEU] (discussing the required trade­off between predictive accuracy and various fairness measures); Alexandra Chouldechova, Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments, 5 Big Data 153, 157 (2017) (demonstrating that recidivism prediction instruments cannot simultaneously meet all fairness criteria where recidivism rates differ across groups because its error rates will be unbalanced across the groups when the instrument achieves predictive parity); Jon Kleinberg et al., Inherent Trade-Offs in the Fair Determination of Risk Scores, 67 LIPIcs 43:1, 43:5–8 (2017), https://drops.dagstuhl.de/opus/volltexte/2017/8156/pdf/LIPIcs-ITCS-2017-43.pdf [https://perma.cc/S9DM-PER2] (demonstrating how difficult it is for algorithms to simultaneously achieve the fairness goals of calibration and balance in predictions involving different groups). ↑
  19. See Bureau of Justice Statistics, U.S. Dep’t of Justice, 2018 Update on Prisoner Recidivism: A 9-Year Follow-up Period (2005–2014) 6 tbl.3 (2018), https://www.bjs.gov/­content/pub/pdf/18upr9yfup0514.pdf [https://perma.cc/3UE3-AS5S] (analyzing rearrests of state prisoners released in 2005 in 30 states and finding that 86.9% of black prisoners and 80.9% of white prisoners were arrested in the nine years following their release); see also Dieterich et al., supra note 13, at 6 (“[I]n comparison with blacks, whites have much lower base rates of general recidivism . . . .”). Of course, the data on recidivism itself may be flawed. This consideration is discussed below. See infra text accompanying notes 33–37. ↑
  20. This is true unless the tool makes no mistakes at all. Kleinberg et al., supra note 17, at 43:5–6. ↑
  21. See infra Section I.A. ↑
  22. For example, Berk et al. consider six different measures of algorithmic fairness. See Berk et al., supra note 17, at 12–15. ↑
  23. See Sam Corbett-Davies & Sharad Goel, The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning (arXiv, Working Paper No. 1808.00023v2, 2018), http://arxiv.org/abs/1808.00023 [https://perma.cc/ML4Y-EY6S]. ↑
  24. See Sumegha Garg et al., Tracking and Improving Information in the Service of
    Fairness (arXiv, Working Paper No. 1904.09942v2, 2019), http://arxiv.org/abs/1904.09942 [https://perma.cc/D8ZN-CJ83]. ↑
  25. See Sam Corbett-Davies et al., Algorithmic Decision Making and the Cost of Fairness, 2017 Proc. 23d ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining 797, 805. ↑
  26. See id. (“[E]xplicitly including race as an input feature raises legal and policy complications, and as such it is common to simply exclude features with differential predictive power.”). ↑
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