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人工智能(AI)信任框架与成熟度模型:运用熵视角提升人工智能的安全性、隐私性及伦理道德性

Artificial Intelligence (AI) Trust Framework and Maturity Model: Applying an Entropy Lens to Improve Security, Privacy, and Ethical AI.

作者信息

Mylrea Michael, Robinson Nikki

机构信息

Department of Computer Science & Engineering, Institute of Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA.

Department of Computer and Data Science, Capitol Technology University, Laurel, ME 20708, USA.

出版信息

Entropy (Basel). 2023 Oct 9;25(10):1429. doi: 10.3390/e25101429.

Abstract

Recent advancements in artificial intelligence (AI) technology have raised concerns about the ethical, moral, and legal safeguards. There is a pressing need to improve metrics for assessing security and privacy of AI systems and to manage AI technology in a more ethical manner. To address these challenges, an AI Trust Framework and Maturity Model is proposed to enhance trust in the design and management of AI systems. Trust in AI involves an agreed-upon understanding between humans and machines about system performance. The framework utilizes an "entropy lens" to root the study in information theory and enhance transparency and trust in "black box" AI systems, which lack ethical guardrails. High entropy in AI systems can decrease human trust, particularly in uncertain and competitive environments. The research draws inspiration from entropy studies to improve trust and performance in autonomous human-machine teams and systems, including interconnected elements in hierarchical systems. Applying this lens to improve trust in AI also highlights new opportunities to optimize performance in teams. Two use cases are described to validate the AI framework's ability to measure trust in the design and management of AI systems.

摘要

人工智能(AI)技术的最新进展引发了人们对伦理、道德和法律保障措施的担忧。迫切需要改进评估人工智能系统安全性和隐私性的指标,并以更符合伦理的方式管理人工智能技术。为应对这些挑战,提出了一种人工智能信任框架和成熟度模型,以增强对人工智能系统设计和管理的信任。对人工智能的信任涉及人类和机器之间就系统性能达成的共识。该框架利用“熵视角”将研究扎根于信息论,并增强对缺乏伦理护栏的“黑箱”人工智能系统的透明度和信任。人工智能系统中的高熵会降低人类的信任,尤其是在不确定和竞争激烈的环境中。该研究从熵研究中汲取灵感,以提高自主人机团队和系统(包括分层系统中的互连元素)的信任和性能。应用这一视角来提高对人工智能的信任,也凸显了优化团队性能的新机会。描述了两个用例,以验证人工智能框架在衡量人工智能系统设计和管理中的信任方面的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d845/10606888/863325c8a45c/entropy-25-01429-g001.jpg

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