mlsanityData to 7 Oct 2026

Survey of Trustworthy AI: A Meta Decision of AI

arXiv 2306.00380 ↗First posted 1 Jun 2023cs.AI cs.CYcited by 0

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Abstract (arXiv)

When making strategic decisions, we are often confronted with overwhelming information to process. The situation can be further complicated when some pieces of evidence are contradicted each other or paradoxical. The challenge then becomes how to determine which information is useful and which ones should be eliminated. This process is known as meta-decision. Likewise, when it comes to using Artificial Intelligence (AI) systems for strategic decision-making, placing trust in the AI itself becomes a meta-decision, given that many AI systems are viewed as opaque "black boxes" that process large amounts of data. Trusting an opaque system involves deciding on the level of Trustworthy AI (TAI). We propose a new approach to address this issue by introducing a novel taxonomy or framework of TAI, which encompasses three crucial domains: articulate, authentic, and basic for different levels of trust. To underpin these domains, we create ten dimensions to measure trust: explainability/transparency, fairness/diversity, generalizability, privacy, data governance, safety/robustness, accountability, reproducibility, reliability, and sustainability. We aim to use this taxonomy to conduct a comprehensive survey and explore different TAI approaches from a strategic decision-making perspective.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 40 of its 159 references that are in the map. First posted on arXiv 1 Jun 2023.

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References · 40

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28 more
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