🔬 Research Summary by Abhishek Roy, a post-doc at Halıcıoğlu Data Science Institute, UC San Diego [Original paper by Abhishek Roy and Prasant Mohapatra] Overview: Designing fair Machine Learning (ML) … [Read more...] about Fairness Uncertainty Quantification: How certain are you that the model is fair?
Technical Methods
On the Impact of Machine Learning Randomness on Group Fairness
🔬 Research Summary by Prakhar Ganesh, incoming Ph.D. student at the University of Montreal and Mila; interested in studying the learning dynamics of neural networks at the intersection of fairness, robustness, privacy, … [Read more...] about On the Impact of Machine Learning Randomness on Group Fairness
Technological trajectories as an outcome of the structure-agency interplay at the national level: Insights from emerging varieties of AI
🔬 Research Summary by Dr. Cristian Gherhes, Founder & CEO of Lexverify and Visiting Fellow at Oxford Brookes University. [Original paper by Cristian Gherhes, Zhen Yu, Tim Vorley, and Lan Xue] Overview: … [Read more...] about Technological trajectories as an outcome of the structure-agency interplay at the national level: Insights from emerging varieties of AI
A hunt for the Snark: Annotator Diversity in Data Practices
🔬 Research Summary by Ding Wang, a senior researcher from the Responsible AI Group in Google Research, specializing in responsible data practices with a specific focus on accounting for the human experience and … [Read more...] about A hunt for the Snark: Annotator Diversity in Data Practices
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
🔬 Research Summary by Zeqiu Wu and Yushi Hu Zeqiu Wu is a final-year PhD student at University of Washington, where she works on language models that converse with and learn from information-seeking … [Read more...] about Fine-Grained Human Feedback Gives Better Rewards for Language Model Training