Reviewer has chosen not to be Anonymous

Overall Impression: Good

Content:
Technical Quality of the paper: Good
Originality of the paper: Yes
Adequacy of the bibliography: Yes

Presentation:
Adequacy of the abstract: Yes
Introduction: background and motivation: Good
Organization of the paper: Satisfactory
Level of English: Satisfactory
Overall presentation: Good

Detailed Comments:

This study is an interesting survey that systematically organizes fairness in machine learning from the perspective of neurosymbolic AI. Many existing bias mitigation methods are designed for specific fairness definition or use cases, and challenges remain in flexibly integrating the complex knowledge, causal relationships, and logical constraints required in real-world applications. In response to these challenges, this research highlights that Neurosymbolic AI, which integrates symbolic reasoning and statistical learning, could serve as a promising framework.

Revisions made in response to previous review comments have improved the structure and readability of the introduction, making the paper’s objectives and scope clearer. Furthermore, the role of Neurosymbolic AI in the pre-processing, in-processing, and post-processing stages of bias mitigation techniques is now more clearly defined. This paper is valuable to the relevant community as it systematically organizes research scattered across the intersection of Neurosymbolic AI and algorithmic fairness, providing a useful reference for researchers working in or entering this area.