✍️ Original article by Abhinav Raghunathan, the creator of EAIDB who publishes content related to ethical ML / AI from both theoretical and practical perspectives. This article is a part of our Ethical AI … [Read more...] about The Ethical AI Startup Ecosystem 04: Targeted AI Solutions and Technologies
Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation
🔬 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
Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support
🔬 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
Let Users Decide: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
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
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices
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