Ethical AI prompt recommendations in large language models using collaborative filtering

2025-10-08

AI Summary

Existing prompt recommendation methods for large language models (LLMs) suffer from insufficient ethical compliance, poor bias controllability, and limited scalability of supervision mechanisms. Method: This paper introduces, for the first time, collaborative filtering into ethically grounded prompt recommendation, proposing a dynamic governance paradigm grounded in user interaction behavior. We construct a controllable synthetic dataset and design a bias-aware collaborative filtering mechanism coupled with a transparency-enhancement module to proactively defend against adversarial prompt engineering. Contribution/Results: Our approach maintains competitive recommendation performance while significantly mitigating systemic biases across dimensions such as gender and race, improving adherence to ethical principles and decision interpretability. Experiments in simulated environments demonstrate superior fairness, robustness, and scalability in prompt recommendation—offering a novel pathway toward responsible AI deployment.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

As large language models (LLMs) shape AI development, ensuring ethical prompt recommendations is crucial. LLMs offer innovation but risk bias, fairness issues, and accountability concerns. Traditional oversight methods struggle with scalability, necessitating dynamic solutions. This paper proposes using collaborative filtering, a technique from recommendation systems, to enhance ethical prompt selection. By leveraging user interactions, it promotes ethical guidelines while reducing bias. Contributions include a synthetic dataset for prompt recommendations and the application of collaborative filtering. The work also tackles challenges in ethical AI, such as bias mitigation, transparency, and preventing unethical prompt engineering.

Problem

• Enhancing ethical prompt selection in LLMs using collaborative filtering
• Mitigating bias and fairness issues in AI prompt recommendations
• Addressing scalability challenges in ethical AI oversight methods

Innovation

• Using collaborative filtering for ethical prompt recommendations
• Leveraging user interactions to reduce bias in LLMs
• Applying recommendation systems to enhance ethical AI guidelines