From features engineering to scenarios engineering for trustworthy AI: I&I, C&C, and V&V

Xuan Li, Peijun Ye, Juanjuan Li, Zhongmin Liu, Longbing Cao, Fei Yue Wang

Research output: Contribution to journalArticlepeer-review

105 Citations (Scopus)

Abstract

Artificial intelligence (AI)'s rapid development has produced a variety of state-of-the-art models and methods that rely on network architectures and features engineering. However, some AI approaches achieve high accurate results only at the expense of interpretability and reliability. These problems may easily lead to bad experiences, lower trust levels, and systematic or even catastrophic risks. This article introduces the theoretical framework of scenarios engineering for building trustworthy AI techniques. We propose six key dimensions, including intelligence and index, calibration and certification, and verification and validation to achieve more robust and trusting AI, and address issues for future research directions and applications along this direction.

Original languageEnglish
Pages (from-to)18-26
Number of pages9
JournalIEEE Intelligent Systems
Volume37
Issue number4
DOIs
Publication statusPublished - 2022
Externally publishedYes

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