Trustworthy Artificial Intelligence: A Review

By Davinder Kaur, Suleyman Uslu, Kaley J. Rittichier, Arjan Durresi. ACM Computing Surveys, 2022

627 citations recorded by the source corpus.

Artificial intelligence (AI) and algorithmic decision making are having a profound impact on our daily lives. These systems are vastly used in different high-stakes applications like healthcare, business, government, education, and justice, moving us toward a more algorithmic society. However, despite so many advantages of these systems, they sometimes directly or indirectly cause harm to the users and society. Therefore, it has become essential to make these systems safe, reliable, and trustworthy. Several requirements, such as fairness, explainability, accountability, reliability, and acceptance, have been proposed in this direction to make these systems trustworthy. This survey analyzes all of these different requirements through the lens of the literature. It provides an overview of different approaches that can help mitigate AI risks and increase trust and acceptance of the systems by utilizing the users and society. It also discusses existing strategies for validating and verifying these systems and the current standardization efforts for trustworthy AI. Finally, we present a holistic view of the recent advancements in trustworthy AI to help the interested researchers grasp the crucial facets of the topic efficiently and offer possible future research directions.

Subfields: Health Informatics, Information Systems, Safety Research

View Paper: https://www.semanticscholar.org/paper/2d93d27fb07fc43bb1e430c37f802586bc9aaf00

How researchers see this paper

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

1. Linyi Yang
2. Shekhar Hansda
3. S. P. Thipperudraswamy
4. Guangsheng Bao
5. Siva Ananth Mariappan
6. Yue Zhang
7. Yanbin Zhao
8. Zhiyang Teng

Citations in the corpus

• Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature (2023, methodology): “Addressing this issue necessitates reliable machine-generated text detection methods (Kaur et al., 2022; Chen & Shu, 2023).”
• Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models (2023, neutral): “Adversarial attacks on Large Language Models (LLMs) (Zou et al., 2023), aiming at manipulating model outputs through input perturbations (Szegedy et al., 2014; Goodfellow et al., 2014) have garnered significant research interest in AI safety (Kaur et al., 2022; Carlini et al., 2021).”
• Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (2022, background): “Several factors, such as fairness, explainability, accountability, reliability, and acceptance, are considered using reliable machine–algorithmcoordinated outcomes [3].”