The Authority of "Fair" in Machine Learning
arXiv:1706.09976
Abstract
In this paper, we argue for the adoption of a normative definition of fairness within the machine learning community. After characterizing this definition, we review the current literature of Fair ML in light of its implications. We end by suggesting ways to incorporate a broader community and generate further debate around how to decide what is fair in ML.
arXiv commentPresented as a talk at the 2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017)
References in corpus (12)
- "Why Should I Trust You?": Explaining the Predictions of Any Classifier
- Supersparse Linear Integer Models for Optimized Medical Scoring Systems
- On the (im)possibility of fairness
- On the relation between accuracy and fairness in binary classification
- A statistical framework for fair predictive algorithms
- Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models
- How to be Fair and Diverse?
- Dice in the Black Box: User Experiences with an Inscrutable Algorithm
- Scalable Bayesian Rule Lists
- Fairness in Reinforcement Learning
- Measuring Fairness in Ranked Outputs
- The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems