1.	While the authors propose optimizing model fairness through the Pareto frontier and simultaneous measurement of multiple fairness indicators, they do not provide a theoretical demonstration of how trading off one fairness metric for another could lead to an overall improvement in model fairness. A deeper theoretical exploration in this area could strengthen the paper, offering clearer guidelines on how to navigate fairness trade-offs effectively.
2.	The paper lacks theoretical analysis on how to select among different Pareto-optimal outcomes, especially when one fairness metric is already at its optimal is one of the Pareto-optimal outcomes, i.e., there is no difference from a single optimization outcome. A theoretical framework or set of criteria for making these choices would be beneficial, providing practitioners with a robust method for decision-making in situations with multiple optimal fairness solutions.
3.	The authors only use one performance to evaluate the model performance. In the context of FairML, where the applications are intricate and multifaceted, relying on a single performance metric may not sufficiently capture the model’s overall performance and impact. A diverse set of performance metrics would provide a more holistic view, ensuring a balanced and thorough evaluation.
4.	In the experimental section, the authors have not conducted comparisons with existing fairness algorithms. Integrating benchmark comparisons against state-of-the-art fairness algorithms would significantly enhance the paper. It would offer tangible evidence of the proposed method's performance and effectively position the ManyFairHPO framework within the existing FairML research landscape.
4. In the experimental section, the authors have not conducted comparisons with existing fairness algorithms. Integrating benchmark comparisons against state-of-the-art fairness algorithms would significantly enhance the paper. It would offer tangible evidence of the proposed method's performance and effectively position the ManyFairHPO framework within the existing FairML research landscape.