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Report from the first AFOG Summer Workshop

Click here for full report as PDF In June of 2018, the Algorithmic Fairness and Opacity Working Group (AFOG) held a summer workshop with the theme “Algorithms are Opaque and Unfair: Now What?.” The event was organized by Berkeley I Read more…

By Jenna Burrell, 1 year5 months ago
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Explainable Machine Learning for Public Policy

NOTE: this reports on an AFOG relevant ISchool final project for the Master of Information Management and Systems (MIMS) program. The project was developed by a student team composed of Samuel Meyer, Shrestha Mohanty, Sung Joo Son, and Monicah Wambugu. Read more…

By Jenna Burrell, 2 years ago
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How might the history of AI help us think about and critique Wang and Kosinsky’s “gaydar” study?

Note: The following is a re-post of AFOG member Shreeharsh Kelkar’s September 25, 2017 post on Scatterplot responding to the controversy over Wang and Kosinski’s (2018) paper about using deep neural networks to recognize “gay” or “straight” faces. At the Read more…

By Daniel Kluttz, 2 years ago
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Seeing Through the Fog

Welcome to the AFOG Blog! We will use this space to post what we hope are accessible and provocative think pieces and reactions to academic research and news stories. Posts about what? Allow us to use this initial blog post Read more…

By Daniel Kluttz, 2 years1 year ago
Recent Posts
  • Report from the first AFOG Summer Workshop
  • Explainable Machine Learning for Public Policy
  • How might the history of AI help us think about and critique Wang and Kosinsky’s “gaydar” study?
  • Seeing Through the Fog
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The Algorithmic Fairness and Opacity Working Group
is located within the School of Information at UC Berkeley, and is funded by a research gift from Google to support cross-disciplinary academic research and conversations between industry and academia to explore and address issues related to fairness and opacity in algorithms.

Website designed and developed by Rohit Raghavan.

RECENT POSTS
  • Report from the first AFOG Summer Workshop
  • Explainable Machine Learning for Public Policy
  • How might the history of AI help us think about and critique Wang and Kosinsky’s “gaydar” study?
  • Seeing Through the Fog
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