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technology Support = Good

Algorithmic Fairness

Supporting means...

Audits algorithms for bias; publishes fairness metrics; addresses discriminatory outcomes; diverse training data; transparent about limitations

Opposing means...

Deploys biased algorithms; ignores discriminatory outcomes; resists audits; lacks diversity in AI development

Recent Incidents

compelled

On August 21, 2026, the Dutch Data Protection Authority (Autoriteit Persoonsgegevens) fined Uber €824,990,000 for violating GDPR's prohibition on fully automated decision-making. Between 2018 and 2022, Uber used software to automatically deactivate driver accounts based on suspected fraud or low customer ratings without human review, and failed to adequately inform drivers that automated decision-making was in operation, causing immediate loss of platform income for affected drivers. The investigation stemmed from a complaint by 171 French drivers via human rights group Ligue des droits de l'Homme to French regulator CNIL, coordinated with the Dutch AP under GDPR's one-stop-shop mechanism since Uber's EU headquarters are in the Netherlands. It is the AP's fourth fine against Uber (following fines in 2018, 2023, and 2024); Uber has appealed the decision.

negligent

26 current and former Meta employees filed a federal lawsuit in the Northern District of California (Oakland) on July 14, 2026, alleging Meta used a 'constellation' of AI systems -- including Metamate, keystroke/activity-monitoring data, AI-token-usage dashboards, and algorithmically assisted performance rankings -- to select employees for the May 2026 layoffs (~8,000 jobs). The suit alleges these output-based metrics 'by design, cannot be accumulated by an employee who is on protected medical or family leave, or whose output is reduced by a disability,' disproportionately targeting workers on protected leave and with disabilities in violation of the ADA, FMLA, and Pregnancy Discrimination Act. Meta said 'workforce management and organisational decisions were and are made by people, not AI.'

xAI filed lawsuit on April 24, 2026 against Colorado AG Philip Weiser challenging the Consumer Protections for AI (CPAI) law (effective June 30, 2026). The law requires developers of 'high-risk' AI systems to exercise 'reasonable care' against algorithmic discrimination. xAI raised six constitutional claims including that developing AI is a First Amendment 'expressive act.' The DOJ joined xAI's lawsuit the same day, alleging the rules 'attempt to force discriminatory ideology on the AI industry.' Civil penalty under the law: $20,000 per violation.

negligent

On May 16, 2025, a court granted conditional certification for Mobley v. Workday to proceed as a nationwide collective action under the Age Discrimination in Employment Act. Derek Mobley claimed Workday's algorithms caused him to be rejected from more than 100 jobs over seven years because of his age, race, and disabilities. Workday disclosed that '1.1 billion applications were rejected' using its software tools, and the collective could potentially include 'hundreds of millions' of members. Workday denies the claims.

negligent

BBC data analysis in December 2024 showed Palestinian news outlets saw 77% decline in engagement after October 7, 2023, while Israeli outlets saw 37% increase. Leaked internal documents revealed Instagram's algorithm was adjusted within a week of October 7th, lowering the moderation confidence threshold for Palestinian content from 80% to 25%, causing significantly more removals.

Under Ton-That's leadership, Clearview AI aggressively expanded its facial recognition platform to more than 3,100 law enforcement agencies across the United States, including the FBI and Department of Homeland Security. By 2024, law enforcement searches via Clearview AI had doubled to 2 million annually. The expansion included a $9.2 million ICE contract in 2025, with ICE personnel using the system globally. This occurred despite wrongful identification cases, including Randal Quran Reid who spent six days in jail due to a mistaken Clearview match.

Koa Health published results of its 2022-2023 ethics audit conducted by Eticas, showing 24% improvement from the prior year. The Koa Foundations app achieved perfect ratings in bias reduction categories with no disparate impact or undesired bias found. The company maintains a public Ethics Impact Assessment framework.

Ng incorporated ethics and responsible AI content into his Machine Learning curriculum on Coursera and DeepLearning.AI, covering fairness, transparency, bias, and societal impact. By embedding these topics into courses taken by millions, he helped establish responsible AI practices as a standard part of technical AI education rather than an afterthought.

compelled

Throughout 2020-2024 multiple US jurisdictions including San Francisco, Boston, Portland (OR), Minneapolis, the entire states of Vermont and Maine, and several New York counties enacted municipal bans on police agency use of facial-recognition systems including Clearview AI, citing documented racial-bias disparities and the absence of constitutional due-process safeguards in algorithmic identification. The ACLU's class-action settlement with Clearview in May 2022 also prohibited the company from selling its database to most private entities in the US.

reactive

In June 2020, after the PULSE AI model depixelated Barack Obama's photo into a white face, LeCun argued that 'ML systems are biased when data is biased' but that 'learning algorithms themselves are not biased.' Timnit Gebru and other researchers criticized this framing as reductive, arguing it ignores systemic issues in AI development. The exchanges became heated, and LeCun signed off Twitter on June 28, 2020, asking 'everyone to please stop attacking each other' and specifically asking people to stop attacking Gebru.

On June 8, 2020, IBM CEO Arvind Krishna sent a letter to Congress announcing IBM would no longer offer, develop, or research facial recognition technology. IBM called for national policies to address racial justice and police reform, becoming the first major tech company to exit the facial recognition market. Krishna stated IBM 'firmly opposes' use of facial recognition for mass surveillance and racial profiling.

negligent

Multiple academic studies found YouTube's recommendation algorithm directed users toward increasingly extreme content. A systematic review found 14 of 23 studies implicated YouTube's recommender system in facilitating problematic content pathways. Research from UC Davis and PNAS showed the algorithm was more likely to recommend extremist and conspiracy content to right-leaning users. Over 70% of content watched on YouTube is recommended by its proprietary, opaque algorithm. While some studies produced contradictory findings, the lack of algorithmic transparency prevented definitive conclusions.

In 2019, The Guardian reported TikTok's moderation practices resulted in removal of content positive toward LGBTQ+ people in countries including Turkey, such as same-sex couples holding hands. In December 2019, TikTok admitted it deliberately reduced the viral potential of videos made by LGBTQ+ users, claiming the goal was to 'reduce bullying' in comments. The Australian Strategic Policy Institute also found content from LGBTQ+ creators was systematically suppressed. While TikTok later updated its policies, the practice demonstrated algorithmic discrimination against marginalized communities under the guise of user protection.

In November 2019, DHH posted a viral thread exposing that the Apple Card algorithm gave him a credit limit 20x higher than his wife's despite her having a longer credit history and higher credit score. Apple co-founder Steve Wozniak confirmed similar disparity. The New York State Department of Financial Services launched an investigation into Goldman Sachs and the Apple Card program as a result.

reactive

After researchers including Kate Crawford documented pervasive bias in ImageNet's person categories -- including racist slurs, misogynist labels, and ableist classifications -- Fei-Fei Li's team systematically identified non-visual concepts and offensive categories. They proposed and executed removal of 1,593 categories (54% of the 2,932 person categories), addressing both bias and privacy concerns in the foundational AI dataset. This represented a significant acknowledgment that even groundbreaking datasets require ongoing ethical review and correction.