Insights From Insurance for Fair Machine Learning
This work provides a conceptual framework for researchers in fair ML, but it is incremental as it builds on existing interdisciplinary insights without introducing new methods or data.
The paper tackles the problem of fairness in machine learning by drawing analogies from insurance literature, offering a fresh perspective that highlights overlooked themes like responsibility and aggregate-individual tensions.
We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and interdisciplinary insurance literature. Tracing the interaction of uncertainty, fairness and responsibility in insurance provides a fresh perspective on fairness in machine learning. We link insurance fairness conceptions to their machine learning relatives, and use this bridge to problematize fairness as calibration. In this process, we bring to the forefront two themes that have been largely overlooked in the machine learning literature: responsibility and aggregate-individual tensions.