Month: October 2020

09
Oct
2020

Capital Punishment–Missing the Forest for the Trees

The Supreme Court has agreed to take on a case out of Kentucky, where a mentally disabled man was convicted of the death penalty. In 2002, the Supreme Court ruled in Atkins v. Virginia, 536 U.S. 304 (2002), that executing a criminal with mental disabilities violates the ban on “cruel and unusual punishment” under the Eighth Amendment. Now, the Court has agreed to hear arguments about whether a mentally disabled person can waive a claim of mental disability, Kentucky v. White, Case No. 20-240.

Let two implications of the Court’s decision to hear arguments on this case sink in. First, the Court had based its Atkins decision on the theory that persons with mental disabilities would not be deterred from committing a capital crime, since they would not understand the ramifications of being subject to the death penalty because of their limited mental capacity. Second, the Court found that society should only demand retribution from those who understood the seriousness of their heinous crimes, and persons with mental disabilities would lake that understanding. If an execution will not further the goals of retribution and deterrence, then it is to be deemed cruel and unusual punishment. That does beg the question: if the criminal cannot understand the serious of his or her crime, how could the criminal have sufficient mental capacity to waive this defense? The Court should be embarrassed to even consider the Catch-22 posed by prosecutors, i.e. that a defendant can be so mentally incapacitated that the defendant should not be executed, but that same defendant must have sufficient mental capacity to proactively waive the right not to be executed.

The second implication of this case is how barbaric it is that the United States is still trying to defend capital punishment. Executing someone is so obviously cruel and unusual that, to deem such actions constitutional, the Court must create limited exceptions and attenuated justifications to the Eighth Amendment proscriptions in order to find any execution constitutional in the 21st Century. That this is an issue that needs a Supreme Court decision shows how picayune are capital punishment standards. This case is nothing more than an attempt by prosecutors to create a “gotcha”–if you are too mentally disabled to know your rights, and you end up with an incompetent attorney because that’s all you could afford on your disability benefits, then prosecutors will have a means to kill you notwithstanding the Eighth Amendment. This would be like claiming you are an environmentalist tree hugger, but then you develop a sufficiently long list of reasons why every tree in that forest can be cut down. The Court should stop trying to rationalize the killing of any person by the government via the exceptions to the Eighth Amendment it is asked to craft, and simply find that every such exception does not create a justifiable basis for circumventing the clear language of the Eighth Amendment. Capital punishment should once and for all be deemed unconstitutional, whether one is old or young, intelligent or impaired, sane or insane, wealthy or poor, White or a Person of Color, etc. etc.

Posted by Alfred Cowger
09
Oct
2020

Obtaining Search Results Without a Warrant—the Patriot Act Strikes Again

Few people noticed in May when the Senate failed by one vote to end the power of the government to search citizens’ internet search records without a warrant. This provision was a part of the Patriot Act, passed in the wake of the 9/11 fears about terrorists plotting under the noses of U.S. security officials. In 2001, few citizens used the internet as they regularly do today, and thus legislators had no idea of the unfettered intrusion into everyday life this provision could wrought. Unfortunately, almost two decades later, when legislators should know better, this blatant violation of the Fourth Amendment was re-authorized rather than dumped in the historical dustpan to join the Japanese interment legislation and Jim Crow laws prohibiting protests by “socialist” labor activists at the turn of the 20th Century.
U.S. history has shown us that the worst time to legislate security measures is in the face of a security threat. During times of national fear, legislators trod over constitutional rights like an elderly person who has fallen in the middle of a human stampede. The Patriot Act passed in the wake of 9/11 is the most recent example of when civil rights succumb to terror—and not just by terrorists. Few people realize that one of the provisions in the Patriot Act empowers the government to view a citizen’s search results without a warrant.
In other words, the average citizen’s daily search of the web for information ranging from financial advice to help with mental health issues, not to mention everyday shopping and socializing, is free for the grabbing by the government. When algorithms are involved, there is no limit to what could be discovered about a person and, worse, how those discoveries could be twisted against someone the government wants to look bad. On one side, every search one does can, via search engine algorithms, lead in directions that the searcher never intended and does not want. On the other side, every search result can, via government algorithms, result in categorizations about a person, and thus conclusions about a person, that may bear little resemblance to that person, but are completely “legitimate” given the design of the algorithm. In fact, given how victims of police brutality have regularly had their private lives, prior records and social contacts smeared in social media by government officials as a defense tactic against those victims’ lawsuits, one should expect government officials to regularly use algorithms to find and twist an individual’s internet searches into dirt, and then to use social media algorithms to spread that dirt quickly and anonymously. Those same algorithms can then be used to expand smear campaigns to virtually every social contact a victim might have via the internet.
I can use myself as example of what could happen. When I was General Counsel of a mass market perfume and cosmetic company, I regularly did internet searches of the company’s trademarks to find instances of trademark infringement and counterfeiting of goods. My searches for “English Leather” turned up so many NSFW sites that I had to be exempted from our IT’s Department’s blocks that prevented the misuse of the company’s network for streaming porn. What if I ended up on an “enemy’s list” of some future administration because of my support of causes and candidates diametrically opposed to that administration?
Without my knowledge and without any limits to the search, the government could unleash an algorithm-based review of all my searches. My completely innocent and rational searches done to protect my employer’s trademarks could easily be used to make me look like someone addicted to sites run by British dominatrixes and BDSM Masters. If those sites happened to use actors who were underage under U.S. law, the fact my trademark search included those sites, even if I had no intention of clicking on the listed links, could expose me to public ridicule and prosecution by government attorneys. Anyone with whom I regularly associate could then be smeared as someone who is a friend of a porn addict and pedophile. My career and private life, as well as those of my friends and business associates, could be ruined as a result of a warrantless search algorithm and a social-media marketing algorithm.
What this demonstrates is that “security” laws passed on emotion rather than reason are likely to have even more heinous ramifications in the Age of Algorithms. In the days of paper records, limitless searches were at least practically limited because voluminous paper records in multiple locations were harder to find and review. Moreover, the government’s misuse of those records might be stopped before the spread became too wide or permanently engrained in the minds of the citizenry. Now, given the ease in which one’s private life can be laid bare, and the ease in which the government can instantaneously and permanently spread mistruths worldwide via the internet, an unfettered government grant of intrusion in the name of security will be far more destructive to individuals’ rights, and thus their lives. If the Fourth Amendment is to survive the Age of Algorithms, legislators and judges should be even more skeptical of police demands for power to search the internet without limitation, and should choose the protection of individual rights over the expansion of police powers.

Posted by Alfred Cowger
09
Oct
2020

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
09
Oct
2020

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