Research Summaries

🔬 Research summary by Connor Wright, our Partnerships Manager. [Original paper by Sasha Costanza-Chock, Inioluwa Deborah Raji, Joy Buolamwini] Overview: The AI audit field is now larger than ever as a … [Read more...] about Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem

🔬 Research Summary by Angelina Wang, a PhD student in computer science at Princeton University studying issues of machine learning fairness and algorithmic bias. [Original paper by Angelina Wang, Vikram V. … [Read more...] about Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation

🔬 Research Summary by Michael A. Madaio, a postdoctoral researcher at Microsoft Research, where his research is at the intersection of HCI and FATE (Fairness, Accountability, Transparency, and Ethics) in AI. … [Read more...] about Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support

Summary contributed by Martin Pawelczyk, a PhD student at the University of Tübingen working on interpretability of machine learning models with a particular focus on algorithmic recourse. [Original paper by … [Read more...] about Let Users Decide: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse

Summary contributed by Yi Sheng, a Ph.D. student at George Mason University, advised by Weiwen Jiang, and interested in software and hardware co-design, AutoML, and dermatology diagnosis. [Original paper by Yi … [Read more...] about The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices