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 | 15 | Subscribers | Read | ai-accountability-review.com |
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Latest Issues
Recent posts by this newsletter. Browse the email archive.
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...
AI Ethics Principles and Accountability
Establishing norms and behavioral standards for AI systems is central to the AI Accountability Problem. Over the years, private companies, government agencies, non-profits, and other organizations have put forth a number of AI ethics princi...
Informing AI Accountability with Public Perceptions
One important way to understand standards and expectations for AI system use and behavior is to ask the public. This is critical especially for calls for accountability in social or media forums since they are most exposed to a plurality of...
Transparency Needs for AI Agent Accountability
AI incident monitoring databases like the OECD AI Incident Monitor and the AI Incident Database track cases where AI has created harm. But by sourcing incidents from public news articles they’re limited in the detail they include. So there’...
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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