<ul><li>The performance of algorithmic decision rules is largely dependent on the quality of training datasets available to them.</li><li>Biases in these datasets can raise economic and ethical concerns due to the resulting algorithms' disparate treatment of different groups.</li><li>The paper proposes algorithms for sequentially debiasing the training dataset through adaptive and bounded exploration.</li><li>The algorithms aim to mitigate the impacts of data biases and achieve more accurate and fairer decisions.</li></ul>

Adaptive Bounded Exploration and Intermediate Actions for Data Debiasing

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