Adaptive Bounded Exploration and Intermediate Actions for Data Debiasing

The performance of algorithmic decision rules is largely dependent on the quality of the training data sets available to them. Biases in these data sets can raise economic and ethical concerns because of the disparate treatment of different groups by the resulting algorithms. In this paper, we propose algorithms for sequentially debiasing the training data set through adaptive and bounded exploration in a classification problem with costly and censored feedback. Our proposed algorithms balance between the ultimate goal of mitigating the impacts of data biases, which will, in turn, lead to more accurate and fairer decisions, and the exploration risks incurred to achieve this goal. Specifically, we propose adaptive bounds to limit the region of exploration and leverage intermediate actions, which provide noisy label information at a lower cost. We analytically show that such exploration can help debias data in certain distributions, investigate how algorithmic fairness interventions can work in conjunction with our proposed algorithms, and validate the performance of these algorithms through numerical experiments on synthetic and real-world data.

History: Accepted by Kaushik Dutta, Area Editor for Responsible AI and Data Science for Social Good, for the INFORMS Journal on Computing Special Issue on Responsible AI and Data Science for Social Goods.

Funding: This work is supported by the National Science Foundation (NSF) program on Fairness in AI in collaboration with Amazon under Award No. IIS-2040800, and by Cisco Research.

Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0651) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.0651). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/.