Grindr, the dating app that caters to gay men, cannot be held responsible for the rape of a 15-year-old boy who the company matched with sexual predators, the U.S. 9th Circuit Court of Appeals ruled this week; it is the latest teens-versus-tech spat in a fight over internet immunity experts say could soon come before the U.S. Supreme Court. The appellate court's ruling upheld a 2023 decision by U.S. District Judge Otis D. Wright II of the Central District of California, who dismissed the suit, saying Grindr was shielded by broad immunity protections passed almost a decade before the plaintiff was born. In a series of events Wright called "alarming and tragic," a closeted Nova Scotia teen downloaded the LGBTQ hookup app in an attempt to meet other gay kids in his rural Canadian town. Instead, over the course of four days, he was assaulted by four adult men, including a man who picked him up after the teen sent him pictures from his high school cafeteria. LGBTQ social networking platform Grindr last year told its all-remote staff they had to return to the office or lose their jobs.

Every day, what's left of the once-mighty ranks of reporters across this country tap out stories meant to inform, entertain and expose. Sometimes they are the work of minutes, the first bits of knowledge on breaking news such as fires, storms or even elections. Sometimes they are investigations that have taken years. Inevitably, as soon as we publish, rich dudes with algorithms come in and sweep this work away for their own profit, like deodorant off a Target shelf. Retail theft is causing a civic meltdown and inspiring a ballot measure to incarcerate repeat toothpaste thieves.

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Abstract: The fields of machine learning and statistics have invested great efforts into designing algorithms, models, and approaches that better predict future observations. Larger and richer data have also been shown to improve predictive power. This is especially true in the world of human behavioral big data, as is evident from recent advances in behavioral prediction technology. Large internet platforms that collect behavioral big data predict user behavior for their internal commercial purposes as well as for third parties, such as advertisers, insurers, security forces, and political consulting firms, who utilize the predictions for user-level personalization, targeting, and other decision-making. While machine learning algorithmic and data efforts are directed at improving predicted values, the internet platforms can minimize prediction error by «pushing» users' actions towards their predicted values using behavior modification techniques.

The fields of statistics and machine learning design algorithms, models, and approaches to improve prediction. Larger and richer behavioral data increase predictive power, as evident from recent advances in behavioral prediction technology. Large internet platforms that collect behavioral big data predict user behavior for internal purposes and for third parties (advertisers, insurers, security forces, political consulting firms) who utilize the predictions for personalization, targeting and other decision-making. While standard data collection and modeling efforts are directed at improving predicted values, internet platforms can minimize prediction error by "pushing" users' actions towards their predicted values using behavior modification techniques. The better the platform can make users conform to their predicted outcomes, the more it can boast its predictive accuracy and ability to induce behavior change. Hence, platforms are strongly incentivized to "make predictions true". This strategy is absent from the ML and statistics literature. Investigating its properties requires incorporating causal notation into the correlation-based predictive environment---an integration currently missing. To tackle this void, we integrate Pearl's causal do(.) operator into the predictive framework. We then decompose the expected prediction error given behavior modification, and identify the components impacting predictive power. Our derivation elucidates the implications of such behavior modification to data scientists, platforms, their clients, and the humans whose behavior is manipulated. Behavior modification can make users' behavior more predictable and even more homogeneous; yet this apparent predictability might not generalize when clients use predictions in practice. Outcomes pushed towards their predictions can be at odds with clients' intentions, and harmful to manipulated users.

When Camille François, a longstanding expert on disinformation, sent an email to her team late last year, many were perplexed. Her message began by raising some seemingly valid concerns: that online disinformation -- the deliberate spreading of false narratives typically designed to sow mayhem -- "could get out of control and become a huge threat to democratic norms". But the text from the chief innovation officer at social media intelligence group Graphika soon became rather more wacky. Disinformation, it read, is the "grey goo of the internet", a reference to a nightmarish, end-of-the world scenario in molecular nanotechnology. The solution the email proposed was to make a "holographic holographic hologram". The bizarre email was not actually written by François, but by computer code; she had created the message -- from her basement -- using text-generating artificial intelligence technology.

AI needs three ingredients: (A) lots of data (B) lots of computing power and (C) modern computing expertise. Internet platforms have all three. Startups rarely either have or can afford (A) and (B) but incumbents can and sometimes don't have (C). So there are natural strategic partnerships to be made. Almost all revenue generating AI is supervised learning.