Technology Category
Natural Language Processing: Ethics — Bias, Fairness, Transparency & PrivacyMachine Learning: Ethics, Bias, and FairnessPhilosophy and Ethics of AI: Bias, Fairness & Equity
Ethical AI prompt recommendations in large language models using collaborative filtering
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
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
Methods, ideas, or system contributions that make the work stand out.
Using collaborative filtering for ethical prompt recommendations
Leveraging user interactions to reduce bias in LLMs
Applying recommendation systems to enhance ethical AI guidelines
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