Quantum-Enhanced Algorithmic Fairness and the Advancement of AI Integrity and Responsibility

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

I've read the paper fully and will briefly address main parts:

1. Introduction
The paper introduces the idea of using quantum computing to address bias in AI systems. It outlines the importance of fairness in automated decision-making and frames the problem within current ethical, legal, and social contexts. The authors propose a framework called Quantum Sentinel, which integrates quantum computing methods into the process of bias detection and mitigation in AI.

2. Literature Review
This section reviews existing work in AI fairness and quantum computing. It identifies that while AI fairness has been extensively studied in classical settings, there is limited work on applying quantum methods to this area. The paper positions its contribution as filling this gap by proposing an approach to use quantum computing to improve fairness in AI.

3. Bias and Fairness Metrics
The authors defne and describe fairness metrics. These mertics are used to measure bias in machine learning outcomes and provide the foundation for evaluating fairness interventions.

4. Quantum Control of Learning Dynamics
The paper introduces quantum physical principles that can be applied to learning systems. concepts such as the quantum Zeno effect and entanglement are suggested as tools to influence learning trajectories and control bias propagation in models. These principles are proposed to act as safeguards against the reinforcement of existing biases during training.

5. Uncertainty and Risk in Quantum Systems
Quantum tools for uncertainty and risk quantification are discussed. The authors suggest using quantum probability ampltudes, von Neumann entropy, and quantum risk matrices to measure the uncertainty and potential risk of decisions made by AI models. This helps to imrpove the transparency and reliability of model outputs.

6. Quantum-Inspired Decision-Making Mechanisms
The paper explores the use of quantum algorithms for decision-making.

7. Integration into CRISP-DM
The Quantum Sentinel framework is mapped onto the CRISP-DM lifecycle. Each stage of the data science workflow—from business understanding to model deployment—is extended with quantum-enhanced tools and checkpoints for fairness assessment and correction.

8. Experimental Results
Simulations are presented using quantum support vector machnes. The authors demonstrate how quantum effects, such as the Zeno effect, can help reduce bias drift over time. 

9. Discussion
The discussion acknowledges the challenges of implementing quantum fairness frameworks in practice. It notes some limitations of quantum hardware and the need for further research into real-world deployment. 

10. Conclusion
The paper concludes that quantum computing offers new opportunities to improve fairness in AI systems.

I think the paper is overall well done, and thus I recommend publication of the article.

Key findings that support this recommendation

• The authors introduce the Quantum Sentinel workflow, mapping quantum bias checks onto every phase of CRISP-DM; this gives practitioners a clear step-by-step path for adoption.

• They formalise bias control with quantum Zeno monitoring and show, through three QSVM simulations, that frequent checks keep fairness metrics near target while accuracy remains stable.

• They link standard fairness metrics to quantum control, which helps readers apply the ideas.

• The discussion states that quantum processing can not only be an accelerator but also a safeguard for privacy and fairness.

However, I strongly recommend that the authors publish the code, if possible, as this is common practice for 21st-century science.
Because the main evidence is simulation-based, it is important that readers can rerun and inspect the experiments and further build on the presented ideas for future research.

Changes I recommend to the manuscript:
-> Several Sections and Subsections start immediately without a single sentence in between; this looks horrible and is horrible to read. Pls find an introduction or a better structuring of these sections. 
6. Decision making in quantum AI: Navigating uncertainty and limited 
information
6.1. Grover’s algorithm in bias detection 

6.7. Integrating Quantum Sentinel with CRISP-DM
6.7.1. Business understanding


-> Pls expand on the following regarding hardware requirements and error sources. If possible, outline the specific hardware requirements associated with your approach. I understand that doing so rigorously would likely require additional simulations (or rather, many many of them) to establish meaningful thresholds. However, this section would benefit from a more detailed discussion on this point. Alternatively, if you prefer to keep the existing listings unchanged, consider introducing a completely new subsection at the end of the paper—or within the conclusion—specifically addressing the quantum hardware aspects of your approach.7.3. Disadvantages of a Quantum Sentinel
• Nascent Technology: Quantum computing is still in its early stages; current hardware
exhibits high error rates and requires extremely controlled environments.

 

Author Response

(Reply is attached.)

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

First of all, I don't understand the reason of trying to use quantum to help AI at this moment. At current stage quantum is still super far from being practical use experimentally. Furthermore, the true advantage of quantum computing is still unknown and it is difficult to tell whether they would be more powerful than classical algorithms in most cases.

Secondly, I don't see a clear reason why applying quantum to AI Integrity and Responsibility is of advantage and the authors do not seem to give me the reasons why they want to do this. From my perspective, it is more like a 'crude' combination of the two hot topics without any clear reasons.

For the points above, I don't believe the paper is reasonably designed.

Author Response

(Reply is attached.)

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Ok. The clarification from the authors makes much sense. I can understand this paper from a perspective point of view. I will give it a pass this time.