Maximizing AI reliability through anticipatory thinking and model risk audits

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ai Magazine Pub Date : 2023-06-23 DOI:10.1002/aaai.12099
Phil Munz, Max Hennick, James Stewart
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引用次数: 0

Abstract

AI is transforming the way we live and work, with the potential to improve our lives in many ways. However, there are risks associated with AI deployments including failures of model robustness and security, explainability and interpretability, bias and fairness, and privacy and ethics. While there are international efforts to define governance standards for responsible AI, these are currently only principles-based, leaving organizations uncertain as to how they can prepare for emerging regulations or evaluate their effectiveness. We propose the use of anticipatory thinking and a flexible model risk audit (MRA) framework to bridge this gap and enable organizations to take an advantage of the benefits of responsible AI. This approach enables organizations to characterize risk at the model level and to apply the anticipatory thinking employed by high reliability organizations to achieve responsible AI deployments.

Abstract Image

通过前瞻性思维和模型风险审计最大限度地提高人工智能的可靠性
人工智能正在改变我们的生活和工作方式,有可能在许多方面改善我们的生活。然而,人工智能部署也存在风险,包括模型稳健性和安全性、可解释性和可解释性、偏见和公平性以及隐私和道德方面的失败。虽然国际上正在努力为负责任的人工智能定义治理标准,但这些标准目前只是基于原则的,这让组织不确定如何为新出现的法规做好准备或评估其有效性。我们建议使用前瞻性思维和灵活的模型风险审计(MRA)框架来弥补这一差距,使组织能够利用负责任的人工智能的优势。这种方法使组织能够在模型层面描述风险,并应用高可靠性组织所采用的前瞻性思维来实现负责任的AI部署。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ai Magazine
Ai Magazine 工程技术-计算机:人工智能
CiteScore
3.90
自引率
11.10%
发文量
61
审稿时长
>12 weeks
期刊介绍: AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. Technical content should be kept to a minimum. In general, the magazine does not publish articles that have been published elsewhere in whole or in part. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and timely columns on topics of interest to AI scientists.
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