Explainability vs. Interpretability in AI: Unraveling the Confusion

In the evolving discourse of Artificial Intelligence (AI), the terms “explainability” and “interpretability” often stir up confusion, used interchangeably without a clear consensus on their definitions. The academic landscape is rife with varied interpretations. For instance, one view sees interpretability as closely related to explanations, while another positions explainability as a broader concept encompassing all efforts to elucidate AI actions. Yet another angle suggests that explainability bridges humans and AI decision-makers through comprehensible interfaces, distinct from interpretability’s role in rendering AI insights meaningful within specific domain knowledge.

This nuanced debate highlights the complexity of making AI understandable to humans. According to emerging insights, explainability is about providing insights tailored to a specific audience’s needs, whether they are domain experts, end-users, or data scientists. These insights, generated by explainability techniques, aim to address various needs such as decision justification, knowledge discovery, AI model enhancement, and fairness assurance. On the other hand, interpretability concerns whether these explanations are coherent and meaningful to the audience’s domain knowledge, questioning the consistency, sense-making capacity, and reasonability of the explanations in supporting decision-making.

As we navigate through the intricacies of AI, understanding the distinction between explainability and interpretability becomes both blurry but also important. It’s about ensuring AI’s transparency and accountability across diverse domains and audiences. The journey towards demystifying AI is ongoing, and embracing these distinctions is a pivotal step forward. Let’s continue to explore and refine our understanding, aiming for AI systems that are not only powerful but also comprehensible and trustworthy.

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