Example Workplace wellness programs and insurance Health data collected through employer wellness programs – framed as a benefit – can flow to insurers and affect how risk is calculated. Example Automated speech recognition and accent A systematic study found that major commercial speech recognition systems had substantially higher error rates for Black speakers than for white speakers. Example Sidewalk Toronto Sidewalk Labs, an Alphabet subsidiary, proposed a sensor-saturated 'smart neighborhood' on Toronto's eastern waterfront. The project was cancelled in 2020 after sustained public opposition that contested not just the privacy implications but the imaginary itself – the idea that cities are optimization problems to be solved through data collection. Example SAG-AFTRA and WGA Strikes Hollywood writers and actors strike, with AI among the central issues – the first major labor actions explicitly about AI's potential to displace creative workers and use their likenesses and work without consent or compensation. Example Pulse oximeters and skin tone Devices used to measure blood oxygen produced falsely reassuring readings for darker-skinned patients during COVID-19 – a problem rooted in how the devices were calibrated. Example The NSA PRISM Surveillance Program Edward Snowden reveals that the NSA has direct access to data held by major technology companies – demonstrating the gap between what users understood their data was used for and what it was actually used for. Example Predictive policing and drug-crime data Predictive policing systems trained on arrest records learned the history of enforcement, not the geography of drug use. Example Period tracking apps and law enforcement Health data collected for personal use became potentially accessible to law enforcement investigating abortion activity after Roe v. Wade was overturned. Example The USA PATRIOT Act and the Architecture of Data Surveillance The PATRIOT Act dramatically expands government authority to access communications and financial records – establishing the legal architecture for large-scale data collection and demonstrating how infrastructure built for one purpose can be repurposed at scale. Example The Netflix Prize Netflix offers $1 million to improve its recommendation algorithm -- and releases 100 million user ratings as the training dataset. Netflix offers $1 million to improve its recommendation algorithm -- and releases 100 million user ratings as the training dataset. Researchers subsequently show the data can be de-anonymized, exposing sensitive attributes users never consented to share. The winning algorithm is never deployed. The competition accelerates machine learning research and generates significant press about Netflix's technical sophistication, while establishing a template platforms have used repeatedly since: release proprietary data as a public challenge, extract free research labor, and retain the data and reputational benefits regardless of outcome. Example Mount Sinai patient records subpoenaed for gender care investigation Medical records collected for adolescent healthcare were subpoenaed by a federal grand jury investigating gender-related treatments – the second Manhattan hospital system to face such a demand. Example ICE and the 30-day reporting window A reporting boundary determined which deaths in immigration detention entered the official record – and a policy change in 2026 moved that boundary back. Example Facebook, Frances Haugen, and the Limits of Engagement Metrics Frances Haugen, a former Facebook product manager, leaks internal documents and testifies before Congress about Facebook's awareness of its systems' effects – raising the question of what a system optimized for engagement is actually optimizing. Example Google Flu Trends Google used flu-related search queries to estimate infection rates – but search behavior is not the same thing as disease. Example The FICO score cutoff Credit scoring models produce a number. Lenders decide what that number means – and where the line falls is a policy choice, not a mathematical result. Example Facial recognition accuracy gaps (Gender Shades) Systematic testing of commercial face analysis systems found error rates up to 34 percentage points higher for darker-skinned women than for lighter-skinned men. Example Face recognition match thresholds and wrongful arrests Three men were wrongfully arrested in part because face recognition systems returned weak matches that analysts treated as identifications. Example The EU AI Act The European Union's AI Act enters into force – establishing the first comprehensive regulatory framework for AI systems, and becoming the reference point for global debates about AI governance. Example ELIZA and the Illusion of Understanding Joseph Weizenbaum builds a program that simulates a therapist by pattern-matching user input and reflecting it back as questions. Users attribute genuine understanding and empathy to a system that has none. Example Credit invisibles and the thin-file trap Millions of Americans – disproportionately Black, Hispanic, and low-income – have no credit file or too thin a file to generate a score. The classification does not merely describe their creditworthiness; it structures the conditions under which creditworthiness becomes possible. Example ProPublica Publishes "Machine Bias" ProPublica's investigation of the COMPAS recidivism prediction tool finds it nearly twice as likely to falsely flag Black defendants as high-risk compared to white defendants – sparking a major public and academic debate about what it means for a predictive system to be fair. Example Clearview AI Clearview AI scrapes billions of images from public websites to build a facial recognition database used by law enforcement – testing the claim that publicly accessible data is available for any use. Example The ChatGPT Release OpenAI releases ChatGPT, making large language model capabilities available to the general public at scale for the first time – triggering a wave of adoption and public debate about work, creativity, education, and truth. Example Facebook data and Cambridge Analytica Data collected through a personality quiz for academic research was used to build political targeting profiles for 87 million users. Example Boston Street Bump app A city used smartphone accelerometers to detect potholes – and produced a map shaped more by who had a phone than by road conditions. Example Blood pressure and the overnight epidemic In 2017, a guideline change reclassified 31 million Americans as hypertensive – without any change in their physiology. What changed was where the threshold was drawn. Example The Asilomar Conference on Recombinant DNA (1975) Concerned about unknown risks of recombinant DNA research, a group of leading molecular biologists called a voluntary moratorium on the most dangerous experiments and convened an international conference to establish biosafety guidelines – before a documented incident had occurred. Example Amazon's Algorithmic Hiring Tool Amazon develops a machine learning tool to screen resumes. Trained on historical hiring data, it learns to penalize resumes associated with women. Amazon discovers the bias internally and abandons the tool in 2018. Example The Allegheny Family Screening Tool A child welfare risk model produces a score from 1 to 20. The county had to decide what score triggers a high-risk flag – and that threshold is a policy choice about which families receive scrutiny. Example Actuarial Risk Tools in Criminal Sentencing Statistical instruments that use group-level factors to predict individual recidivism appear in US sentencing and parole decisions – establishing the template for algorithmic decision-making in high-stakes institutional contexts, and the arguments about fairness that persist today.