Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning
This work addresses a conceptual issue in fair machine learning for researchers and practitioners, but it is incremental as it builds on existing critiques without introducing new methods or data.
The paper tackles the problem of overgeneralization in fair machine learning, where different forms of discrimination (e.g., racism, sexism) are treated interchangeably, and argues for greater context specificity to improve fairness and address overlooked harms.
A key value proposition of machine learning is generalizability: the same methods and model architecture should be able to work across different domains and different contexts. While powerful, this generalization can sometimes go too far, and miss the importance of the specifics. In this work, we look at how fair machine learning has often treated as interchangeable the identity axis along which discrimination occurs. In other words, racism is measured and mitigated the same way as sexism, as ableism, as ageism. Disciplines outside of computer science have pointed out both the similarities and differences between these different forms of oppression, and in this work we draw out the implications for fair machine learning. While certainly not all aspects of fair machine learning need to be tailored to the specific form of oppression, there is a pressing need for greater attention to such specificity than is currently evident. Ultimately, context specificity can deepen our understanding of how to build more fair systems, widen our scope to include currently overlooked harms, and, almost paradoxically, also help to narrow our scope and counter the fear of an infinite number of group-specific methods of analysis.