Summary of Advancing Interactive Explainable Ai Via Belief Change Theory, by Antonio Rago and Maria Vanina Martinez
Advancing Interactive Explainable AI via Belief Change Theory
by Antonio Rago, Maria Vanina Martinez
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to interactive explainable AI (XAI) methods using belief change theory. The authors argue that this formal foundation can provide a principled methodology for developing interactive explanations, ensuring warranted behavior, transparency, and accountability. They introduce a logic-based formalism to represent explanatory information shared between humans and machines, and demonstrate its applicability in real-world scenarios with varying prioritizations of new and existing knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers create a new way to make AI explainable using a theory called belief change. This helps people understand how AI makes decisions and why it’s making certain choices. The authors show that this approach can be used in real-life situations where humans give feedback to AI systems. They also discuss the challenges of applying this theory to different situations. |
