Is quality only important in AI?
https://www.bespacific.com/persuadability-and-llms-as-legal-decision-tools/
Persuadability and LLMs as Legal Decision Tools
Persuadability and LLMs as Legal Decision Tools. Oisin Suttle. School of Law and Criminology, Maynooth University, Maynooth Ireland. David Lillis School of Computer Science University College Dublin Dublin Ireland (2026). As Large Language Models (LLMs) are proposed as legal decision assistants, and even first-instance decision-makers, across a range of judicial and administrative contexts, it becomes essential to explore how they answer legal questions, and in particular the factors that lead them to decide difficult questions in one way or another. A specific feature of legal decisions is the need to respond to arguments advanced by contending parties. A legal decision-maker must be able to engage with, and respond to, including through being potentially persuaded by, arguments advanced by the parties. Conversely, they should not be unduly persuadable, influenced by a particularly compelling advocate to decide cases based on the skills of the advocates, rather than the merits of the case. We explore how frontier open- and closed-weights LLMs respond to legal arguments, reporting original experimental results examining how the quality of the advocate making those arguments affects the likelihood that a model will agree with a particular legal point of view, and exploring the factors driving these results. Our results have implications for the feasibility of adopting LLMs across legal and administrative settings.
[…] across our full range of models, the identity of the advocate model (and hence the quality of the argument presented) has an average effect of between 8% and 21%, implying stronger Advocate models typically win between 58% and 71% of the time. As between the strongest and weakest Advocate models, depending on the Judge model, those win rates range from 63% to over 90%. We therefore conclude that all our Judge models are to some quite substantially persuadable…
Could become useful…
https://pogowasright.org/resource-u-s-state-data-broker-laws-comparison-chart/
RESOURCE: U.S. State Data Broker Laws Comparison Chart
David Stauss of Stauss Law writes:
Key point: Our new chart compares the data broker laws of California, Connecticut, Nevada, Oregon, Texas, and Vermont, covering applicability standards, registration and disclosure obligations, consumer rights, and penalties.
State data broker laws are proliferating, and they vary widely in scope and structure. Connecticut recently passed a data broker registration law similar to California’s existing law with statutory penalties of $200 per day, per violation. Meanwhile, Vermont significantly amended its law to create new bonding, due diligence, and breach notification requirements.
To help track these differences, we built a comparison chart covering:
How each state defines “data broker” and what information is covered
Applicability thresholds
Annual registration and disclosure requirements
Whether the law creates substantive consumer rights or a delete/opt-out mechanism
Other notable provisions
Penalty structures
The chart is designed as a quick-reference tool for compliance counseling and issue spotting. You can find the chart here and in our Resource Center.
Your results may vary…
Four in five under-16s in Australia using social media despite ban, study shows
… Australia is the first country to ban social media for children. Since December 2025, under-16s have been prohibited from having accounts with many social media platforms including TikTok, X, Facebook, Instagram, YouTube and Snapchat.
But an observational study of 408 12- to 17-year-olds by the country’s University of Newcastle has concluded that Australia’s social media minimum age legislation has resulted in “limited implementation, incomplete compliance, and substantial circumvention of social media restrictions”.
(Related)
https://thenextweb.com/news/tiktok-youtube-indonesia-child-accounts
TikTok and YouTube cut 4.7 million under-16 accounts in Indonesia
… The regulation issued in March is the legal engine behind the numbers. It requires companies running platforms the government deems high risk to deactivate accounts belonging to children under 16, and it places the burden of enforcement on the platforms themselves rather than on parents or schools.
That design, a state-set threshold enforced by the companies under threat of penalty, is the same broad approach Australia has taken with its under-16 ban, and the two cases are increasingly read together as tests of whether platform-led enforcement can be made to work at national scale.
… The mechanics of enforcement, whether by age declaration, behavioural signals, or other means, were not spelled out in the announcement, which leaves open the same question that has dogged similar measures elsewhere: how many barred users simply return under a new account and an older stated age.
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