708 citations · 735 across the 3 of their papers we have counts for
5 papers
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
Flavien Prost, Ben Packer, Jilin Chen +9
There has been a flurry of research in recent years on notions of fairness in ranking and recommender systems, particularly on how to evaluate if a recommender allocates exposure e…
LaMDA: Language Models for Dialog Applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall +57
We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters an…
Re-imagining Algorithmic Fairness in India and Beyond
Nithya Sambasivan, Erin Arnesen, Ben Hutchinson +2
Conventional algorithmic fairness is West-centric, as seen in its sub-groups, values, and methods. In this paper, we de-center algorithmic fairness and analyse AI power in India. B…
Fairness in Recommendation Ranking through Pairwise Comparisons
Alex Beutel, Jilin Chen, Tulsee Doshi +8
Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information. As such it…
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
Alex Beutel, Jilin Chen, Tulsee Doshi +6
As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address i…