Adaptive Data Debiasing through Bounded Exploration and Fairness
- URL: http://arxiv.org/abs/2110.13054v1
- Date: Mon, 25 Oct 2021 15:50:10 GMT
- Title: Adaptive Data Debiasing through Bounded Exploration and Fairness
- Authors: Yifan Yang and Yang Liu and Parinaz Naghizadeh
- Abstract summary: Biases in existing datasets used to train algorithmic decision rules can raise ethical, societal, and economic concerns.
We propose an algorithm for sequentially debiasing such datasets through adaptive and bounded exploration.
- Score: 19.082622108240585
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Biases in existing datasets used to train algorithmic decision rules can
raise ethical, societal, and economic concerns due to the resulting disparate
treatment of different groups. We propose an algorithm for sequentially
debiasing such datasets through adaptive and bounded exploration. Exploration
in this context means that at times, and to a judiciously-chosen extent, the
decision maker deviates from its (current) loss-minimizing rule, and instead
accepts some individuals that would otherwise be rejected, so as to reduce
statistical data biases. Our proposed algorithm includes parameters that can be
used to balance between the ultimate goal of removing data biases -- which will
in turn lead to more accurate and fair decisions, and the exploration risks
incurred to achieve this goal. We show, both analytically and numerically, how
such exploration can help debias data in certain distributions. We further
investigate how fairness measures can work in conjunction with such data
debiasing efforts.
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