With great power comes great responsibility…
https://www.bespacific.com/artificial-intelligence-and-aggregate-litigation/
Artificial Intelligence and Aggregate Litigation
Wilf-Townsend, Daniel, Artificial Intelligence and Aggregate Litigation (March 01, 2025). 103 Wash. U. L. Rev. __ (forthcoming 2026), Available at SSRN: https://ssrn.com/abstract=5163640 or http://dx.doi.org/10.2139/ssrn.5163640
The era of AI litigation has begun, and it is already clear that the class action will have a distinctive role to play. AI-powered tools are often valuable because they can be deployed at scale. And the harms they cause often exist at scale as well, pointing to the class action as a key device for resolving the correspondingly numerous potential legal claims. This article presents the first general account of the complex interplay between aggregation and artificial intelligence. First, the article identifies a pair of effects that the use of AI tools is likely to have on the availability of class actions to pursue legal claims. While the use of increased automation by defendants will tend to militate in favor of class certification, the increased individualization enabled by AI tools will cut against it. These effects, in turn, will be strongly influenced by the substantive laws governing AI tools—especially by whether liability attaches “upstream” or “downstream” in a given course of conduct, and by the kinds of causal showings that must be made to establish liability. After identifying these influences, the article flips the usual script and describes how, rather than merely being a vehicle for enforcing substantive law, aggregation could actually enable new types of liability regimes. AI tools can create harms that are only demonstrable at the level of an affected group, which is likely to frustrate traditional individual claims. Aggregation creates opportunities to prove harm and assign remedies at the group level, providing a path to address this difficult problem. Policymakers hoping for fair and effective regulations should therefore attend to procedure, and aggregation in particular, as they write the substantive laws governing AI use.
What if the AI hates me?
The worries about AI in Trump’s social media surveillance
As the Trump administration goes after immigrants for allegedly posing national security threats, social media posts have taken a prominent role in the story — coming up in the Department of Homeland Security’s allegations against Palestinian activist Mahmoud Khalil, the Georgetown University researcher Badar Khan Suri and alleged gang member Jerce Reyes Barrios.
It’s not clear what tools the government is using to collect and analyze social media posts, and DHS didn’t respond to a direct request about how it is surveilling online platforms.
… Earlier social media monitoring tools functioned more like a search engine, surfacing and ranking results based on relevancy, but AI tools take on a more deterministic role, Rachel Levinson-Waldman, the managing director of the Brennan Center’s Liberty and National Security Program, told DFD.
“AI is starting to be used, not just to streamline the process, which already brings its own significant concerns, but to augment or replace the judgment,” said Levinson-Waldman, who studies social media monitoring tools.
… “There are real concerns that AI is being used to automate target selection, and potentially initiating surveillance without adequate human review,” Kia Hamadanchy, a senior policy counsel for the American Civil Liberties Union, told the committee.
The use of AI in social media surveillance also creates greater potential for what experts call automation bias. The term describes a tendency to trust technology to deliver accurate information — an issue that has surfaced in healthcare, aviation and law enforcement.
(Related)
UK creating ‘murder prediction’ tool to identify people most likely to kill
The UK government is developing a “murder prediction” programme which it hopes can use personal data of those known to the authorities to identify the people most likely to become killers.
Researchers are alleged to be using algorithms to analyse the information of thousands of people, including victims of crime, as they try to identify those at greatest risk of committing serious violent offences.
The scheme was originally called the “homicide prediction project”, but its name has been changed to “sharing data to improve risk assessment”. The Ministry of Justice hopes the project will help boost public safety but campaigners have called it “chilling and dystopian”.
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