SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 4: A comparison of baseline and R-TD3 policies in a purely synthetic environment with the objective of maximizing a weighted combination of the cumulative true positive and true negative fractions of a binary classification task (Section 4), subject to three fairness constraints (Section 5.1) (columns). In all cases, the baseline, greedy policies drive the system (indicated by streamlines) to a 1-dimensional manifold in the space of group qualification rates that is bounded away from universal qualification (upper right corner of each plot), while the RL agents are generally able to drive the system closer to this favorable equilibrium. Shading represents local violations of fairness.
D.4 Maximize True-Positives  $+0.8$  True-Negatives; Synthetic Data
Table 5: A comparison of baseline and R-TD3 policies in a semi-synthetic environment incorporating data synthesized from the UCI "Adult Data Set" (Dua and Graff, 2017), as detailed in Appendix A.2. Policies share the objective of maximizing a weighted combination of the cumulative true positive and true negative fractions of a binary classification task (Section 4), subject to three fairness constraints (Section 5.1) (columns). This figure indicates a negative result, in which the R-TD3 algorithm does not offer clear benefits over the baseline policies. As noted in the main text, there are no theoretical guarantees for the R-TD3 algorithm, and the possibility of such negative results constitute a risk for real-world deployments.
D.5 Maximize True-Positives  $+0.8$  True-Negatives; "Adult" Data
D.6 Training Curves: L-UCBFair
D.6.1
D.7 Training Curves: R-TD3
D.8 Reduction of Utility