Book contents
- Frontmatter
- Contents
- Contributors
- Acknowledgments
- Part I Introduction
- Part II Taxonomy of Metacognitive Approaches
- Part III Neuro-Symbolic Models in AI
- Part IV Metacognition with LLMS
- Part V Metacognition in Learning Agents
- Part VI Assured Machine Learning in High-Stakes Domains
- Part VII Metacognition as a Solution to Handle Failure
- 13 Reasoning about Anomalous Object Interaction Using Plan Failure as a Metacognitive Trigger
- 14 Tractable Probabilistic Reasoning for Trustworthy AI
- Part VIII Applications of Metacognitive AI
14 - Tractable Probabilistic Reasoning for Trustworthy AI
from Part VII - Metacognition as a Solution to Handle Failure
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Book contents
- Frontmatter
- Contents
- Contributors
- Acknowledgments
- Part I Introduction
- Part II Taxonomy of Metacognitive Approaches
- Part III Neuro-Symbolic Models in AI
- Part IV Metacognition with LLMS
- Part V Metacognition in Learning Agents
- Part VI Assured Machine Learning in High-Stakes Domains
- Part VII Metacognition as a Solution to Handle Failure
- 13 Reasoning about Anomalous Object Interaction Using Plan Failure as a Metacognitive Trigger
- 14 Tractable Probabilistic Reasoning for Trustworthy AI
- Part VIII Applications of Metacognitive AI
Summary
One of the central aspects of metacognitive AI is the AI agent’s ability to reason about its own behavior. In particular, for AI systems to be deployed in real-world applications with high impact, it is crucial that we can reason about and guarantee their fairness and robustness. Here, we provide a probabilistic reasoning framework to audit and enforce fairness of automated decision-making systems, using classifiers as the main example, while being robust to uncertainties and noise in the distribution.
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- Metacognitive Artificial Intelligence , pp. 245 - 256Publisher: Cambridge University PressPrint publication year: 2025
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