The Pragmatic Turn in Explainable Artificial Intelligence (XAI)

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The Pragmatic Turn in Explainable Artificial Intelligence (XAI) Minds and Machines, 29(3), 441-459 DOI: 10.1007/s11023-019-09502-w Please quote the printed version Andr?s P?ez Universidad de los Andes [email removed] ABSTRACT In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions ...

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• [1] https://arxiv.org/abs/2002.09595