Balancing Trustworthiness and Efficiency in Artificial Intelligence Systems: An Analysis of Tradeoffs and Strategies

IF 3.7 4区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

IEEE Internet Computing Pub Date : 2023-11-01 DOI:10.1109/mic.2023.3303031

Yifei Wang

引用次数: 0

Abstract

As artificial intelligence (AI) systems become more prevalent in various domains, ensuring their trustworthiness and efficiency becomes increasingly important. This article presents an analysis of the tradeoffs between different dimensions of trustworthy AI, including transparency, robustness, fairness, accountability, efficiency, privacy, and human interaction. The challenges and opportunities that arise when striving for a balance among these dimensions are also discussed. Recommendations for achieving an optimal balance are also offered. By understanding and addressing these tradeoffs, we can foster AI systems that perform efficiently but also maintain user trust and societal values.

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人工智能系统中诚信与效率的平衡:权衡与策略分析

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

IEEE Internet Computing 工程技术-计算机：软件工程

CiteScore

7.60

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： This magazine provides a journal-quality evaluation and review of Internet-based computer applications and enabling technologies. It also provides a source of information as well as a forum for both users and developers. The focus of the magazine is on Internet services using WWW, agents, and similar technologies. This does not include traditional software concerns such as object-oriented or structured programming, or Common Object Request Broker Architecture (CORBA) or Object Linking and Embedding (OLE) standards. The magazine may, however, treat the intersection of these software technologies with the Web or agents. For instance, the linking of ORBs and Web servers or the conversion of KQML messages to object requests are relevant technologies for this magazine. An article strictly about CORBA would not be. This magazine is not focused on intelligent systems. Techniques for encoding knowledge or breakthroughs in neural net technologies are outside its scope, as would be an article on the efficacy of a particular expert system. Internet Computing focuses on technologies and applications that allow practitioners to leverage off services to be found on the Internet.