A Means-End Account of Explainable Artificial Intelligence
This provides a conceptual framework for researchers and practitioners in XAI to better align explanations with specific needs, though it is incremental in nature.
The paper tackles the lack of consensus in explainable AI (XAI) by proposing a means-end account that structures the field based on different topics, stakeholders, and goals, showing how this leads to a taxonomy and normative assessment of methods.
Explainable artificial intelligence (XAI) seeks to produce explanations for those machine learning methods which are deemed opaque. However, there is considerable disagreement about what this means and how to achieve it. Authors disagree on what should be explained (topic), to whom something should be explained (stakeholder), how something should be explained (instrument), and why something should be explained (goal). In this paper, I employ insights from means-end epistemology to structure the field. According to means-end epistemology, different means ought to be rationally adopted to achieve different epistemic ends. Applied to XAI, different topics, stakeholders, and goals thus require different instruments. I call this the means-end account of XAI. The means-end account has a descriptive and a normative component: on the one hand, I show how the specific means-end relations give rise to a taxonomy of existing contributions to the field of XAI; on the other hand, I argue that the suitability of XAI methods can be assessed by analyzing whether they are prescribed by a given topic, stakeholder, and goal.