In the course of recent developments, the development of good AI has become a challenge for society as a whole, and explainability is generally considered to be a central factor in achieving this. However, I assume that (i) explainability is not of absolute, but only of instrumental value. What follows practically is that (ii) explainability is not needed at all times and in all places, but only when something has become questionable in a given context. Now, for some deployments there seem to be little variances, e.g. seeking robust knowledge in research , building-trust for innovative business , or ensuring accountability in healthcare . Yet other popular applications fields such as autonomous vehicles (AV) face a variety of different context and social constellations. Consequently, we need to start by asking under what conditions, for whom, why and what for is explainability is (expected to be) useful ? To do justice to this situated explanability, I will (iii) not follow the logic of scientific explanations, but conceptualize explanation as a social process that can analytically be grasped through the interplay and dynamics of four constitutive components (explainer, explainee, explanadum, explanans) . Here, I will examine three particular cases: (a) explainability for optimizing an experimental vehicle (the AutoNOMOS project as reported by ), (b) explainability for solving liability issues (the proposition of an “Ethical Black Box” ), and (c) explainability to handle mixed-traffic-situations (external human-vehicle-interfaces ). In so doing, I want to make a plea for a richer understanding of explainability than the tradition of the Hempel-Oppenheim scheme  can offer.
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