Algorithms and historical discrimination

The British Secondary School Test Debacle—When Algorithms Are Designed to Churn Out a Result, not a Rational Conclusion

Few Americans have heard of the standardized test disaster that occurred this year in Britain, and has shaken both the British education system and the Johnson government. This disaster should be considered a warning when any entity, in particular the government, wants to use an algorithm to justify a result rather than to make objective determinations.
This debacle started with a seemingly beneficial result in mind. The British education system had long been accused of “grade creep”, such that more top marks were being given out than was warranted by the quality of those students receiving the marks. To make matters worse, that creep seemed to be favoring students of the upper classes who attend the most posh public (i.e. private, to the confusion of the average U.S. citizen) high schools. After all, parents pay good money to ensure that by attending those exclusive secondar schools, their children will be more likely to be admitted to the best universities in the British Isle.
In response to these criticisms, the British government hired designers to develop an algorithm that would prevent grade creep. The algorithm would determine the percentage of students that “should” fall within each grade range. Furthermore, each student’s expected grade as given by a teacher would be evaluated using both historical results for students with similar schooling as that student, as well as expected results for all students taking tests that year. Those historical results and expected results were, in turn, based on algorithmic analysis. If the student’s final grade given by a teacher deviated from that expected grade, the algorithm could override the teacher’s grade and raise or lower the student’s score.
The resulting re-grading was so disastrous the government had no choice but to scrap the entire plan and fall back on the teachers’ initial test scores. Those students who came from schools with historically low test scores found their grades lowered, notwithstanding their personal achievement. Students from schools with historically high test score results, particularly those in small classes—in other words the upper class private schools– found their scores revised upwards. The alterations were so clearly unfair, and affected so many students striving to perform better than society assumed of them, that the algorithm results were deemed clearly unfair and biased, notwithstanding the fact these algorithms were incredibly intricate in design, because they were to be tools to overcome unfairness and bias. In fact, the algorithm creators wrote a 317-page report explaining just how fair and objective the algorithm results would be. See Will Bedingfield, Everything that went wrong with the botched A-levels algorithm, WIRED (Aug 19, 2020), https://www.wired.co.uk/article/alevel-exam-algorithm,
So what went wrong? The complicated answer is the many problems were to be expected, given the complexity of the algorithm. The simple answer is that this outcome is a prime example of when governments design and use algorithms to reach a desired outcome, rather than use algorithms to reach a proper outcome. Moreover, this demonstrates what happens when algorithms use a bell curve to define outcomes—those persons who have traditionally fallen outside the norms which establish the bell curve are those who are most detrimentally affected by the forced outcomes required by a bell curve. Finally, this proves clearly that algorithms will go wrong. Even when a majority of the algorithm’s determinations are accurate, no algorithm will be perfectly accurate. When thousands of people, like the British graduating student population, are affected by an algorithm, the number harmed by inevitably accurate results could likewise be in the thousands, even with the best algorithm. As this debacle shows, algorithms that are “just good” will result in too many individuals actually harmed.
Finally, one must remember what could have happened if government officials had not acted. How would the average student be able to protest his or her wrongful treatment? They could never prove how the algorithm harmed them, or perhaps even if they were indeed one of the individuals harmed, because the process was so non-transparent. The government, in fact, could easily establish that for “most” students, the results were acceptably accurate. Inevitably, the government would be buttressed by experts paid by the algorithm designer to argue the algorithm was acceptable. Students would face discrimination, as well as harm from arbitrary and unreasonable results, which would clearly be constitutional but for the fact the students would not have the resources to meet their burden of proof. In fact, even with substantial resources, given the Black Box nature of algorithms, the students still would never be able to meet their burden of proof, meaning that the use of the algorithms by the government was sure to preclude any student’s due process rights. This debacle is a foreshadowing of both the harm that could befall recipients of government benefits and determinations in the Age of Algorithms, and the inevitable deprivation of constitutional rights that will preclude those harmed from ever being made whole.

Posted by Alfred Cowger

Defeating the Fair Credit Act with Algorithms as Proposed in New Trump Regulations

The Trump Administration is proposing new regulations under the Fair Housing Act that will turn the worst aspects of algorithms against plaintiffs who would otherwise have a case of housing discrimination under the Fair Housing Act. Currently, a plaintiff who was denied a housing loan, or whose offer to buy a house or rent an apartment was denied, can prove a violation of the Fair Housing Act by showing the lender or property owner’s regular denials resulted in a “disparate impact” against minorities or women. Thus, a plaintiff need not prove the defendant harbored an intent to discriminate, but can let the results of the defendant’s actions, in essence, speak for themselves. As reported by David Gershgorn in a OneZero post in Medium, https://onezero.medium.com/a-proposed-trump-administration-rule-could-let-lenders-discriminate-through-a-i-2f9a729b0f3c, HUD wants to pass regulations that will allow discriminators to avoid evidence of disparate impact simply by using a well-designed algorithm as the tool to discriminate.
In my book (see listing under “Publications”), I warn that algorithms could quickly become a tool to rationalize all sorts of discriminatory actions, such that plaintiffs will be unable to prove discrimination because a defendant employs an algorithm to undertake that discrimination. Algorithms work in what Prof. Frank Pasquale has called a “Black Box”. No one can be sure what data an algorithm has used to reach its conclusions, nor can anyone know the process by which an algorithm used that data to reach its conclusion. In fact, the more sophisticated the algorithm, the more its “machine learning” capabilities will obfuscate how it reached its conclusions, since it will have taught itself the most expedient way to reach those conclusions, regardless of what the algorithm’s designer initially intended. To make matters worse, the databases used by algorithms are often infected with decades of discriminatory results, and algorithms have a nasty tendency to “learn” of past discrimination and actually employ that discrimination as an “efficient” way to reach a conclusion. After all, what is easier for an algorithm than denying a housing loan the moment the algorithm determines an applicant is a minority, a woman or a resident of an area with higher historical rates of mortgage defaults?
HUD should be working on regulations to prevent algorithms from becoming 21st Century tools of red-lining. Unfortunately, it is doing the exact opposite. HUD is proposing that disparate impact claims can be defeated by discriminating lenders and property owners simply by using algorithms to make the discriminatory decisions for those lenders and owners. The regulations would create five elements that would be defenses against disparate impact claims. One of those elements would be that the algorithm was designed to use “objective” criteria to reach its conclusions. Another would allow the algorithm user to simply hire an expert to opine that the algorithm seems to be working objectively. Both elements are simply masks by which algorithm-based discrimination is already occurring, and thus should be subject to regulations against their use, not regulations supporting their use.
As my book details, given the tendency of algorithms to discriminate, along with the Black Box nature of algorithms that precludes proving via direct evidence that the algorithm’s process was discriminatory, algorithms are the perfect tool to obfuscate discrimination otherwise demonstrated by statistical evidence in disparate impact claims. In fact, this is just the latest example of where governments have hidden behind algorithms to discriminate, ranging from criminal sentencing to child welfare investigations. The algorithm industry is quite happy to provide the experts to testify how objective those algorithms “really” are in the face of clear evidence of disparate impact, even though it begs the question that, if a plaintiff can’t determine how an algorithm reached its discriminatory result, how those experts can testify that they have a basis for their opinions when they are likewise clueless to how the algorithm reached its result. If these regulations are enacted, they could be the start of a destructive use of algorithms to render any disparate impact claim impotent.

Posted by Alfred Cowger