AI Accountability Review
Nick DiakopoulosThe AI Accountability Review translates research to help bridge the gap between knowledge and practice for AI policymakers, practitioners, and researchers.
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 20 | Subscribers | Read | ai-accountability-review.com |
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Latest Issues
Recent posts by this newsletter. Browse the email archive.
From Explanation to Accountability
In the event of an incident where an AI system causes harm, the people responsible for that system may be expected to render an explanation to various accountability forums, such as the media, administrative bodies, or in a courtroom. This ...
LLMs Can’t Provide Faithful Explanations Needed for AI Accountability
A growing array of research points out that the explanations produced by LLMs are not accurate. In the literature this is referred to as explanation faithfulness (Agarwal et al, 2024; Jacovi and Goldberg, 2020) and accurately measuring it i...
Experimenting with AI in a Living Literature Review
The AI Accountability Review (AIAR) is a living literature review with the goal of tracking literature on the topic of AI accountability for an audience of researchers and policymakers. I write posts that either focus on translating a singl...
A Critique of Transparency Provisions in NY’s RAISE Act (1.0)
The US has a couple of state laws—from California, and now New York—that address the risk of frontier AI models. Both broadly operate by specifying some information about frontier AI models that must be disclosed for the purposes of oversig...
Authors
The writers behind this newsletter.
Professor at Northwestern University. I study Computational Journalism as well as AI, Ethics, and Society.
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