Machine learning is increasingly used to make decisions about people, and there have been several reports of bias in areas such as recidivism assessment, job-applicant screening and mortgage risk. This master’s thesis focused on hyperparameters, the many settings that govern how a model learns. These settings are usually tuned to maximise overall performance, such as accuracy, even though they also affect how fairly the model treats groups of people, for example based on race, gender or age. The thesis developed a framework that adds fairness as a goal when tuning these settings and tested it in a fraud detection case study.
The work led to an article presenting Fairband, a fairness-aware algorithm for tuning hyperparameters. Tested on four real-world decision-making datasets, Fairband consistently found models with substantially better fairness at a comparatively small cost in accuracy, with no extra training cost. The authors describe it as simple, resource-efficient and easy to integrate into real-world machine learning pipelines.
Developed at Feedzai Research, the thesis won the Best Thesis Award in the field of Information Technology. According to Feedzai, Fairband increases the impartiality of automated decision-making processes by an average of 93%. Although this work does not focus on healthcare, it addresses a question that is also central to medical AI: how to make sure automated decisions do not disadvantage some groups of people.
Learn more about the thesis here: https://hdl.handle.net/10216/128959
Learn more about the article here: https://doi.org/10.48550/arXiv.2010.03665
Please note: the article is a preprint, which means it has not yet been peer reviewed.
References:
Cruz, A. M. F. (2020). Fairness-aware hyperparameter optimization [Master’s thesis, Universidade do Porto]. Repositório Aberto da Universidade do Porto.
Cruz, A. F., Saleiro, P., Belém, C., Soares, C., & Bizarro, P. (2020). A bandit-based algorithm for fairness-aware hyperparameter optimization (arXiv:2010.03665). arXiv.