Social Cooling (2017) (www.socialcooling.com)

“Social Cooling” describes how ubiquitous data collection and predictive scoring are producing chilling effects on behavior: when people feel watched, they self‑censor, avoid risk, and conform. Databrokers and opaque machine learning models now infer thousands of intimate attributes (from religion and sexual orientation to health, personality and political views) by correlating disparate data points. Those scores are automatically integrated into hiring, advertising, lending, insurance, dating and even medical oversight—producing real harms such as biased job placements, differential ad exposure for women, worse loan terms for people with “bad” social ties, and perverse medical incentives that penalize doctors who take on high‑risk patients. The technical and societal implications are significant: automated, black‑box predictive models create tight feedback loops and incentives that amplify bias and entrench conformity at scale. This isn’t just privacy loss but a systemic change in behavior and institutions—reducing free speech, innovation and social mobility. The essay argues we must treat Social Cooling like an environmental crisis: raise public awareness rapidly, build cross‑disciplinary policy and technical solutions (algorithmic transparency, anti‑discrimination safeguards, and rights like “forgetting”), and act within years rather than decades to preserve the ability to err, dissent and take risk in a data‑driven society.