Conclusion

Our paper extends the analysis of two other scenarios that have been previously studied in the academic ML fairness literature. The ML-fairness-gym framework is also flexible enough to simulate and explore problems where “fairness” is under-explored. For example, in a supporting paper, “Fair treatment allocations in social networks,” we explore a stylized version of epidemic control, which we call the precision disease control problem, to better understand notions of fairness across individuals and communities in a social network.