[論文レビュー] Perspectives on the Social Impacts of Reinforcement Learning with Human Feedback

Gabrielle Kaili-May Liu|arXiv (Cornell University)|Mar 6, 2023

Ethics and Social Impacts of AI被引用数 8

ひとこと要約

論文は人間のフィードバックを用いた強化学習（RLHF）の社会的・倫理的影響を調査し、潜在的な積極的な社会影響とガバナンスの課題を強調する。RLHFは整合性を改善し、偏見を減らし、公平なアクセスを広げる可能性がある一方、乱用とガバナンスのギャップに対して注意を喚起している。

ABSTRACT

Is it possible for machines to think like humans? And if it is, how should we go about teaching them to do so? As early as 1950, Alan Turing stated that we ought to teach machines in the way of teaching a child. Reinforcement learning with human feedback (RLHF) has emerged as a strong candidate toward allowing agents to learn from human feedback in a naturalistic manner. RLHF is distinct from traditional reinforcement learning as it provides feedback from a human teacher in addition to a reward signal. It has been catapulted into public view by multiple high-profile AI applications, including OpenAI's ChatGPT, DeepMind's Sparrow, and Anthropic's Claude. These highly capable chatbots are already overturning our understanding of how AI interacts with humanity. The wide applicability and burgeoning success of RLHF strongly motivate the need to evaluate its social impacts. In light of recent developments, this paper considers an important question: can RLHF be developed and used without negatively affecting human societies? Our objectives are threefold: to provide a systematic study of the social effects of RLHF; to identify key social and ethical issues of RLHF; and to discuss social impacts for stakeholders. Although text-based applications of RLHF have received much attention, it is crucial to consider when evaluating its social implications the diverse range of areas to which it may be deployed. We describe seven primary ways in which RLHF-based technologies will affect society by positively transforming human experiences with AI. This paper ultimately proposes that RLHF has potential to net positively impact areas of misinformation, AI value-alignment, bias, AI access, cross-cultural dialogue, industry, and workforce. As RLHF raises concerns that echo those of existing AI technologies, it will be important for all to be aware and intentional in the adoption of RLHF.

研究の動機と目的

• Systematically study the social effects of RLHF.
• Identify key social and ethical issues arising from RLHF.
• Discuss social impacts of RLHF for various stakeholders.
• Argue for a net-positive social impact of continued RLHF development.

提案手法

• Literature synthesis of RLHF concepts and historical context.
• Discussion of seven primary social impact areas of RLHF.
• Evaluation of potential benefits and risks in information integrity, alignment, bias, access, culture, industry, and work and labor.

実験結果

リサーチクエスチョン

• RQ1 How might RLHF affect information integrity and trust in AI-generated content?
• RQ2 How can RLHF reflect diverse values and preferences across populations?
• RQ3 In what ways can RLHF mitigate or magnify social inequalities and access to AI?
• RQ4 What are the cultural, international, and workforce implications of RLHF?
• RQ5 What governance and mitigation strategies are proposed to counter misuses of RLHF?

主な発見

• RLHF has potential to combat misinformation by improving truthfulness and reducing toxicity compared to non-HF models like GPT-3.
• RLHF enhances value alignment by guiding models to follow explicit instructions and implicit human values, aiding inner alignment and safety.
• RLHF can mitigate various biases and promote equitable access by enabling smaller models with less compute and data requirements.
• Cross-cultural feedback through RLHF can support culturally aware AI deployment and peaceful dialogue across contexts.
• RLHF bolsters industry applications and workforce transformation, while raising concerns about governance, safety, equity, and access to powerful models.