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 | 18 | Subscribers | Read | ai-accountability-review.com |
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Latest Issues
Recent posts by this newsletter. Browse the email archive.
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...
Closing Information Gaps via AI Transparency
Before anyone can be held accountable for an AI system’s behavior we’re going to need some information about that system. What was the system’s behavior and was its performance unexpected? What are the underlying values and goals of its des...
Gaps in First-Party and Third-Party AI Model Evaluations
A group of researchers with the EvalEval Coalition recently published a new paper on arXiv: “Who Evaluates AI’s Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations” where they present an analysis of evaluations of...
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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