立场：弥合机器遗忘与AI监管之间的差距
Position: Bridge the Gaps between Machine Unlearning and AI Regulation
AI总结：
本立场论文指出机器遗忘在辅助AI监管（如欧盟AIA）合规方面存在技术现状与实际应用间的差距，系统梳理了潜在应用场景并标明了技术缺口，呼吁研究人员解决这些开放问题以释放其潜力。
摘要：
“被遗忘权”及其所体现的数据隐私法律，自早期以来就推动了机器遗忘（machine unlearning）的发展。如今，有人认为，即将到来的人工智能监管浪潮——如欧盟的《人工智能法案》（AIA）——可能为机器遗忘提供重要的新用例。然而，本立场论文认为，只有当研究人员主动弥合机器遗忘的技术现状与其在AI监管中的潜在应用之间（有时相当大的）差距时，这一机遇才能得以实现。为了论证这一点，我们以AIA作为主要案例研究。具体而言，我们就机器遗忘当前在辅助遵守AIA各项条款方面的潜力（或在许多情况下缺乏潜力），提供了“遗忘现状”的评估。这始于对机器遗忘在AIA合规方面潜在应用的精确梳理。针对每一项应用，我们标明了存在于潜在应用与机器遗忘技术现状之间的技术差距。最后，我们发出行动呼吁：呼吁机器学习研究人员解决这些开放的技术问题，这些问题一旦解决，将能够释放机器遗忘在辅助遵守AIA及其他类似AI监管方面的潜力。
英文摘要：
The ''right to be forgotten'' and the data privacy laws that encode it have motivated machine unlearning since its earliest days. Now, some argue that an inbound wave of artificial intelligence regulations -- like the European Union's Artificial Intelligence Act (AIA) -- may offer important new use cases for machine unlearning. However, this position paper argues, this opportunity will only be realized if researchers proactively bridge the (sometimes sizable) gaps between machine unlearning's state of the art and its potential applications to AI regulation. To demonstrate this point, we use the AIA as our primary case study. Specifically, we deliver a ``state of the union'' as regards machine unlearning's current potential (or, in many cases, lack thereof) for aiding compliance with various provisions of the AIA. This starts with a precise cataloging of the potential applications of machine unlearning to AIA compliance. For each, we flag the technical gaps that exist between the potential application and the state of the art of machine unlearning. Finally, we end with a call to action: for machine learning researchers to solve the open technical questions that could unlock machine unlearning's potential to assist compliance with the AIA -- and other AI regulations like it.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
