š¬ Research Summary by Przemyslaw Grabowicz, a Research Assistant Professor of Computer Science at the University of Massachusetts Amherst. [Original paper by Przemyslaw Grabowicz, Nicholas Perello, Aarshee … [Read more...] about Fair and explainable machine learning under current legal frameworks
Research Summaries
Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection and Use in the Pursuit of Fairness
š¬ Research Summary by Sarah Villeneuve, a Program Lead working on Fairness, Transparency, and Accountability at the Partnership on AI. [Original paper by McKane Andrus and Sarah Villeneuve] Overview: Most … [Read more...] about Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection and Use in the Pursuit of Fairness
Promises and Challenges of Causality for Ethical Machine Learning
š¬ Research Summary by Aida Rahmattalabi, a PhD in computer science at the University of Southern California where she focused on developing data-driven and trustworthy algorithms to guide public health … [Read more...] about Promises and Challenges of Causality for Ethical Machine Learning
An Algorithmic Introduction to Savings Circles
š¬ Research Summary by Christian Ikeokwu, a PhD Student in Computer Science at UC Berkeley with a focus on Theoretical Computer Science and Artificial Intelligence. [Original paper by Rediet Abebe, Adam Eck, … [Read more...] about An Algorithmic Introduction to Savings Circles
(Re)Politicizing Digital Well-Being: Beyond User Engagements
š¬ Research Summary by Niall Docherty and Asia Biega. Dr Niall Docherty is focused on analyzing, critiquing, and building āhealthyā sociotechnical systems, currently at Microsoft Research New England, and, from … [Read more...] about (Re)Politicizing Digital Well-Being: Beyond User Engagements