论文标题
提高数据效率的社会和治理含义
Social and Governance Implications of Improved Data Efficiency
论文作者
论文摘要
许多研究人员致力于提高机器学习的数据效率。如果他们成功会发生什么？本文探讨了提高数据效率的社会经济影响。具体来说，我们研究了数据效率将侵蚀进入现有数据丰富的人工智能公司的进入的障碍，从而使它们暴露于来自数据贫困公司的更多竞争中的直觉。我们发现这种直觉仅是部分正确的：数据效率使创建ML应用程序变得更加容易，但是大型AI公司可能从高性能的AI系统中获得更多收益。此外，我们发现对隐私，数据市场，鲁棒性和滥用的影响很复杂。例如，虽然滥用风险随着数据效率而增加似乎是直观的 - 随着越来越多的参与者获得任何水平的能力，净效应至关重要地取决于改善了防御措施。对数据效率的更多调查以及对“ AI生产功能”的研究将是了解AI行业及其社会影响的关键。
Many researchers work on improving the data efficiency of machine learning. What would happen if they succeed? This paper explores the social-economic impact of increased data efficiency. Specifically, we examine the intuition that data efficiency will erode the barriers to entry protecting incumbent data-rich AI firms, exposing them to more competition from data-poor firms. We find that this intuition is only partially correct: data efficiency makes it easier to create ML applications, but large AI firms may have more to gain from higher performing AI systems. Further, we find that the effect on privacy, data markets, robustness, and misuse are complex. For example, while it seems intuitive that misuse risk would increase along with data efficiency -- as more actors gain access to any level of capability -- the net effect crucially depends on how much defensive measures are improved. More investigation into data efficiency, as well as research into the "AI production function", will be key to understanding the development of the AI industry and its societal impacts.
