19 citations · 20 across the 4 of their papers we have counts for
4 papers
Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set
Gordon Dai, Pavan Ravishankar, Rachel Yuan +2
When selecting a model from a set of equally performant models, how much unfairness can you really reduce? Is it important to be intentional about fairness when choosing among this…
The Legal Duty to Search for Less Discriminatory Algorithms
Emily Black, Logan Koepke, Pauline Kim +2
Work in computer science has established that, contrary to conventional wisdom, for a given prediction problem there are almost always multiple possible models with equivalent perf…
Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features
Hadi Elzayn, Emily Black, Patrick Vossler +3
The vast majority of techniques to train fair models require access to the protected attribute (e.g., race, gender), either at train time or in production. However, in many importa…
Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools
Emily Black, Rakshit Naidu, Rayid Ghani +3
While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias often gets reduced to enforcing an arbitrarily chosen fairness metric, either by e…