By Kuniko Paxton
Review Details
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: Limited
Organization of the paper: Needs improvement
Level of English: Satisfactory
Overall presentation: Average
Detailed Comments:
Overall
This manuscript is a comprehensive and narrative survey on neurosymbolic AI for fairness, and it is particularly interesting for its discussion of the relationship between bias mitigation and neurosymbolic methods. This work addresses an important gap: although several studies have applied neurosymbolic approaches to fairness problems, it remains unclear how logical rules and fairness constraints can be integrated into neural networks and what kinds of effects these approaches provide. Section 4 clearly explains the relationship between neurosymbolic methods and bias mitigation, and Section 5 demonstrates the characteristics of the neurosymbolic approach through experiments. On the other hand, the introduction and Sections 3 would benefit from clearer positioning of the paper’s main motivation and consistency.
Introduction
While the introduction presents ADM primarily in the context of public policy, it remains somewhat unclear whether the paper focuses specifically on neurosymbolic fairness models for public services.
In addition, the frequent use of supplementary explanations and examples in parentheses interrupts the argumentative flow and reduces readability.
Section 3 (Bias Mitigation from a Neurosymbolic Perspective)
A substantial portion of the paper is devoted to reviewing conventional bias mitigation techniques in pre-processing, in-processing, and post-processing. As a result, the central novelty of the paper, the neurosymbolic perspective on fairness and bias mitigation, becomes less prominent. Condensing the general survey and allocating more space to neurosymbolic-specific architectures, constraint representations, and reasoning mechanisms would strengthen the paper’s contribution and better highlight its originality. This would also help distinguish the paper more clearly from existing survey papers on general fairness and bias mitigation techniques for classification models.
