Adaptive Bounded Exploration and Intermediate Actions for Data Debiasing
This work addresses data bias issues in algorithmic decision-making, which can lead to unfair treatment of different groups, but it is incremental as it builds on existing debiasing and exploration methods.
The paper tackles the problem of algorithmic decision rules being affected by biases in training datasets by proposing algorithms for sequentially debiasing data through adaptive and bounded exploration in classification with costly feedback. The result includes analytical proofs that such exploration can debias data in certain distributions and validation through numerical experiments on synthetic and real-world data.
The performance of algorithmic decision rules is largely dependent on the quality of training datasets available to them. Biases in these datasets can raise economic and ethical concerns due to the resulting algorithms' disparate treatment of different groups. In this paper, we propose algorithms for sequentially debiasing the training dataset 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.