My paper, "Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI", with @andrewthesmart (Google Research, @staeiou (UCSD), and Abigail Z. Jacobs (University of Michigan), is now in the Harvard Data Science Review! https://t.co/X7Yg450JBu
The determinants of safety are not social/organizational or technical alone, but sociotechnical. Disasters provide a good reference for this, as they mark the moments when complex systems receive exquisite scrutiny. Astounding parallels echo across accidents in many domains.
My paper, "Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI", with @andrewthesmart (Google Research, @staeiou (UCSD), and Abigail Z. Jacobs (University of Michigan), is now in the Harvard Data Science Review! https://t.co/X7Yg450JBu
By treating these as first-order engineering concerns, it is possible to make order out of the chaos of AI evaluation and safety techniques and tools. Without treating safety as a system property, for example by focusing on component reliability/performance, failure follows.
Our #CHI2023 paper *From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML* led by our incredible intern @ShalalehRismani will be presented on Tue, April 25 @ 17:17
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New series on #AI & procurement featuring a wide-ranging exchange of ideas by @AshleyCasovan, @az_jacobs, Carlos Ignacio Gutierrez, Cary Coglianese, @dchenok, @DMulliganUCB, David Rubenstein, @JTillipman, @realjoshkroll, Lavi Ben Dor, Tim Cooke & @var0101. https://t.co/9uvWXXSLHn
Responsible AI systems must comply with applicable law, mitigate risk to stakeholders, and eliminate structural biases, argues @realjoshkroll of @NPS_Monterey. https://t.co/LQUNmONoro
Just out! New https://t.co/7QDmdtLF0t on #FacialRecognition says its "fundamental limitations" create "profound #privacy and ethical challenges," system bias is "pervasive and profound," and FR is "insufficiently trustworthy" to replace human ID. Online at https://t.co/DPRyK0F2Jo
Pleased to release a new paper, “Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems”, to appear in the 2021 @FAccTConference. https://t.co/bfCRLhDuvk.
@math_rachel All insight about measurement problems in ML is due to the amazing @az_jacobs and @hannawallach, who have a forthcoming paper on this topic that we should have referenced more clearly. A critical and often overlooked deep subject worth significant attention and study!