Filling gaps in trustworthy development of AI
Filling gaps in trustworthy development of AI
复制标题
填补人工智能可信发展的空白
作者：
S. Avin;Haydn Belfield;Miles Brundage;Gretchen Krueger;Jasmine Wang;Adrian Weller;Markus Anderljung;Igor Krawczuk;David M. Krueger;Jonathan Lebensold;Tegan Maharaj;Noa Zilberman
事件共享、审计和其他具体机制可以帮助验证行为者的可信度人工智能（AI）的应用范围很广，潜在的危害也很大。人们对人工智能系统潜在风险的认识不断提高，促使人们采取行动解决这些风险，同时也削弱了人们对人工智能系统和开发这些系统的组织的信心。2019年的一项研究（1）发现，已有80多家组织发布并采用了“人工智能伦理原则”，此后又有更多的组织加入。但这些原则往往在值得信赖的人工智能开发的“什么”和“如何”之间留下了差距。这种差距使可疑或道德可疑的行为成为可能，这对特定组织的可信度产生了怀疑，并在更广泛的领域。因此，迫切需要一种具体的方法，既能使人工智能开发人员防止伤害，又能让他们通过可验证的行为来证明自己的可信度。下面，我们将探索[来自（2）]的机制，以创建一个AI开发人员可以赢得信任的生态系统-如果他们值得信赖（见图）。更好地评估开发人员的可信度可以为用户选择、员工行为、投资决策、法律的追索权和新兴的治理机制提供信息。
Description Incident sharing, auditing, and other concrete mechanisms could help verify the trustworthiness of actors The range of application of artificial intelligence (AI) is vast, as is the potential for harm. Growing awareness of potential risks from AI systems has spurred action to address those risks while eroding confidence in AI systems and the organizations that develop them. A 2019 study (1) found more than 80 organizations that have published and adopted “AI ethics principles,” and more have joined since. But the principles often leave a gap between the “what” and the “how” of trustworthy AI development. Such gaps have enabled questionable or ethically dubious behavior, which casts doubts on the trustworthiness of specific organizations, and the field more broadly. There is thus an urgent need for concrete methods that both enable AI developers to prevent harm and allow them to demonstrate their trustworthiness through verifiable behavior. Below, we explore mechanisms [drawn from (2)] for creating an ecosystem where AI developers can earn trust—if they are trustworthy (see the figure). Better assessment of developer trustworthiness could inform user choice, employee actions, investment decisions, legal recourse, and emerging governance regimes.