Papers

As artificial intelligence, particularly generative AI, continues to reshape labor markets, traditional AI safety frameworks prioritize existential and technical risks while overlooking critical human-centric challenges. This position paper advo- cates for a paradigm shift towards a pro-worker governance framework that ad- dresses the systemic risks posed by AI on economic justice and labor rights. We identify six key risks, including the exacerbation of technical debt, disproportion- ate job displacement, and the monopolistic tendencies of AI firms. By propos- ing actionable interventions such as collective licensing for AI-generated content, mandatory AI watermarking, and robust retraining policies, we aim to enhance the resilience of labor markets. This paper calls for an inclusive dialogue among stakeholders, emphasizing the need for policies that not only safeguard against the adverse effects of AI but also promote shared prosperity. Our framework aims to establish a sustainable relationship between AI and labor that empowers workers and fosters equitable growth.

The rapid increase in submissions to AI conferences has led to a crisis in the peer review process, characterized by declining review quality and accountability. This position paper proposes a novel bi-directional feedback mechanism where authors can evaluate the quality of reviews while safeguarding against retaliation. Cou- pled with a blockchain-enabled reviewer rewards system, this framework aims to incentivize high-quality reviewing and create an accountability structure that ben- efits all stakeholders. By allowing authors to provide feedback on reviews and rewarding reviewers with transparent digital credentials, this system fosters a cul- ture of quality and responsibility in the peer review process. We call upon the AI community to engage in this vital conversation and explore these transformative reforms for sustainable peer review practices.

The rapid increase in submissions to AI conferences has led to a crisis in the peer review process, characterized by declining review quality and accountability. This position paper proposes a novel bi-directional feedback mechanism where authors can evaluate the quality of reviews while safeguarding against retaliation. Cou- pled with a blockchain-enabled reviewer rewards system, this framework aims to incentivize high-quality reviewing and create an accountability structure that ben- efits all stakeholders. By allowing authors to provide feedback on reviews and rewarding reviewers with transparent digital credentials, this system fosters a cul- ture of quality and responsibility in the peer review process. We call upon the AI community to engage in this vital conversation and explore these transformative reforms for sustainable peer review practices.

How will language models (LMs) affect future economic progress? Inspired by the Lever of Riches by Mokyr (1992), we argue that the institutions governing LM content generation and usage patterns are critical to answering this question. We content that, because LM creators have a strong incentive to collect, train, and deploy intellectual property protection, the all-you-can-consume access to knowledge and creativity they enable has led to rapid acceptance and widespread use, which in turn results in smaller, low-skill employment creation but increased output and greater overall welfare. We provide a theoretical and analytical framework explaining this phenomenon and point to its long-term consequences using empirical evidence.

本文探讨AI是否具有意识这一前沿问题。通过建立一套评估体系，收集整理最新研究结果，对AI的意识水平进行打分评估。基于哲学、神经科学和心理学三个维度的综合分析，结果显示当前AI意识的整体支持度约为43.84%。直观的结果图表可访问 acw.gixia.org 查看。