Trustworthy AI: From Principles to Practices

By Bo Li, Peng Qi, Bo Liu, Shuai Di, Jingen Liu, Jiquan Pei, Jinfeng Yi, Bowen Zhou. ACM Computing Surveys, 2021

660 citations recorded by the source corpus.

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment of various systems based on it. However, many current AI systems are found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection. These shortcomings degrade user experience and erode people’s trust in all AI systems. In this review, we provide AI practitioners with a comprehensive guide for building trustworthy AI systems. We first introduce the theoretical framework of important aspects of AI trustworthiness, including robustness, generalization, explainability, transparency, reproducibility, fairness, privacy preservation, and accountability. To unify currently available but fragmented approaches toward trustworthy AI, we organize them in a systematic approach that considers the entire lifecycle of AI systems, ranging from data acquisition to model development, to system development and deployment, finally to continuous monitoring and governance. In this framework, we offer concrete action items for practitioners and societal stakeholders (e.g., researchers, engineers, and regulators) to improve AI trustworthiness. Finally, we identify key opportunities and challenges for the future development of trustworthy AI systems, where we identify the need for a paradigm shift toward comprehensively trustworthy AI systems.

Subfields: Artificial Intelligence, Safety Research

View Paper: https://www.semanticscholar.org/paper/70043a0b612b6253b37df7d363b3bf2ec3d581c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philippe Schwaller
2. Dejing Dou
3. Haoyi Xiong
4. Xiao Zhang
5. Xuanyu Wu
6. Jiang Bian
7. Ji Liu
8. Xuhong Li

In Safety Research

1. Dejing Dou

Citations in the corpus

• Augmenting large language models with chemistry tools (2023, background): “Although the current results are limited by the amount and quality of the chosen tools, the space of possibilities is vast, particularly as potential tools are not restricted to the chemistry domain.”
• Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond (2021, background): “Researchers [29, 47, 81, 101, 107, 183] also proposed many other desiderata for interpretations, interpretation algorithms or interpretability, such as fairness, privacy, reliability, robustness, causality, trust, fidelity, faithfulness, transferability, informativeness, transparency, plausibility, ”