Different Ways to Understand the Act of Explaining in XAI
Introduction to Speech Acts in Explanations
Explanation types in XAI play a key role in many fields, from artificial intelligence (AI) to education and law. To understand how explanations work, we can look at speech act theory. This theory breaks communication down into three types: illocutionary, perlocutionary, and locutionary acts. Each of these types gives us a different way to see and assess explanations.
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Illocutionary Acts: These focus on the speaker’s intent. For example, a teacher explains a concept to help students understand. In legal settings, clarity and meeting legal standards matter most. The goal is for the explanation to meet specific needs.
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Perlocutionary Acts: Here, the emphasis is on the effect on the listener. In education, an effective explanation changes a learner’s understanding or actions. Since people respond differently, these acts are harder to measure objectively.
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Locutionary Acts: Locutionary acts are about the actual content and wording of the explanation. In Explainable AI (XAI), the goal is often just to clarify a model’s decision-making, even if it’s not personalized.
How Context Shapes Explanation Types in XAI
Different fields shape explanations in their own ways.
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Legal Context: In law, illocutionary acts dominate. Explanations need to convey clear, legally-compliant information that anyone involved can understand. For example, explaining an AI decision in legal terms should clearly outline the reason behind it and use simple language.
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Educational Context: In education, perlocutionary effects matter most. Explanations work best when they change how the learner thinks or acts. This is why teachers adjust explanations based on the audience’s background and comprehension level.
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XAI Context: In XAI, explanations are often locutionary, with a focus on showing how a model works. They may not need to account for a listener’s level of understanding but instead aim to reveal specific steps in the model’s process.
How These Interpretations Affect Evaluation
Evaluating explanations depends on their context and the purpose they serve. Each interpretation leads to different metrics.
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Legal Evaluation Metrics: Here, illocutionary principles matter most. Explanations must meet standards for clarity and completeness, and they must satisfy legal guidelines. They don’t need to change listener behavior, just meet information needs clearly.
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Educational Evaluation Metrics: In education, metrics are more subjective. Engagement, comprehension, and knowledge application all help assess the explanation. Learners respond differently, so personalized evaluation is key.
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XAI Evaluation Metrics: XAI metrics often focus on accuracy and clarity. Here, an explanation works if it shows model insights, even if it’s not user-centered. But focusing only on locutionary content might miss the user’s actual understanding.
Bridging Gaps in Explanation Types in XAI
Combining these perspectives can help improve explanations in all fields, especially in XAI:
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Customized Explanations: XAI systems can combine clarity (locutionary), rationale (illocutionary), and user concerns (perlocutionary). This layered approach better meets audience needs.
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User-Centered Design: Involving users in the design process helps meet their needs directly. User feedback can improve XAI explanations, making them clearer and more relevant.
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Cross-Discipline Collaboration: Collaboration between AI, education, law, and cognitive science experts brings diverse insights into designing effective explanations. This leads to better frameworks that meet each field’s unique needs.
Conclusion
Explaining in XAI involves a mix of illocutionary, perlocutionary, and locutionary acts. By understanding these differences, researchers and designers can create better, more relevant explanations. This raises an important question: Can Explainability Improve Human Learning? The result? Greater transparency, user trust, and ethical AI practices in explanation types in XAI.
As XAI evolves, so will our understanding of explanations. To keep up, we need to refine evaluation metrics, focus on user-centered designs, and work across disciplines. This way, XAI explanations can remain clear, informative, and impactful.
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