Paper short abstract:

Despite abundant Explainable AI (xAI) literature, controversy is notably absent. Our study categorizes 12,000+ xAI papers, uncovering heterogeneity along technical, empirical and ontological dimensions. We urge a renewal of STS methods to build a critical discourse on this field.

Paper long abstract:

The upcoming successes of deep learning based systems in critical fields (medicine, military, public services) is conducive to serious concerns on the interpretability and accountability of its outcomes. Therefore, the research production on “Explainable AI” (xAI) should raise considerable scientific controversy and social debate.

In contrast, this communication emphasizes the actual almost non-existence of controversy emerging from the development of the xAI literature. Even though, in 2016, DARPA’s “Explainable AI program” was followed by a sudden appearance of scientific publications on xAI, those generally framed xAI as a technical problem rather than an epistemological and political one.

Exploring this paradox between an abundant literature on xAI and an absence of controversy, we intend to open the black box of self-appointed AI-explainers. Our presentation thus urges a renewal of STS methodologies to establish a critical typology of xAI techniques. Our methodology was twofold: we first systematically categorized 12,000+ papers in the xAI research field, then proceeded to an analysis of the mathematical content of a representatively diversified sample. As a first result, we show that xAI methods come considerably diversified. We summarize this diversity in a 3-dimensional typology: technical dimension (what kind of calculation is used?), empirical dimension (what is being looked at?) and ontological dimension (what makes the explanation right?) standpoints.

The heterogeneity of those techniques not only illustrates disciplinary specificities, but also shows that the research field on xAI progresses rather autonomously and opportunistically with primary objectives to fuel market strategies and answer the institutional demand for explanation