Transforming human interactions with AI via reinforcement learning with human feedback (RLHF)
Authors: GKM Liu
Published: 2024
Publication: Massachusetts Institute of Technology, 2023 - computing.mit.edu
The paper explores Reinforcement Learning with Human Feedback (RLHF) as a transformative tool to align AI with human values, mitigate bias, and democratize technology, while emphasizing its societal implications and ethical considerations.
Methods: The paper employs a systematic study of existing and potential societal effects of RLHF, guided by key questions addressing ethical, social, and practical impacts.
Key Findings: The study investigates how RLHF affects information integrity, societal values, social equity, access to AI, cultural relations, industrial transformation, and labor dynamics.
Limitations: The study is conceptual and lacks empirical experimentation, limiting its ability to provide quantified or concrete evidence for its claims.
Institution: Massachusetts Institute of Technology
Research Area: Reinforcement Learning with Human Feedback (RLHF) , Human-AI Interaction
Discipline: Artificial Intelligence
Citations: 17