As Christian List (2021) has recently argued, the increasing arrival of powerful AI systems that operate autonomously in high-stakes contexts creates a need for “future-proofing” our regulatory frameworks, i.e., for reassessing them in the face of these developments. One core part of our regulatory frameworks that dominates our everyday moral interactions is blame. Therefore, “future-proofing” our extant regulatory frameworks in the face of the increasing arrival of powerful AI systems requires, among others things, that we ask whether it makes sense (...) to extend our blaming practices to these systems. In the paper, we argue for the admittedly surprising thesis that this question should be answered in the affirmative: contrary to what one might initially think, it can make a lot of sense to blame AI systems, since, as we furthermore argue, many of the important functions that are fulfilled by blaming humans can also be served by blaming AI systems. The paper concludes that this result gives us a good pro tanto reason to extend our blame practices to AI systems. (shrink)

In Being Realistic About Reasons,T.M. Scanlon develops a non-naturalistic realist account of normative reasons. A crucial part of that account is Scanlon’s contention that there is no deep epistemological problem for non-naturalistic realists, and that the method of reflective equilibrium suffices to explain the possibility of normative knowledge. In this critical notice we argue that this is not so: on a realist picture, normative knowledge presupposes a significant correlation between distinct entities, namely between normative beliefs and normative facts. This correlation (...) calls for an explanation. We show that Scanlon does not have the resources to offer such an explanation. (shrink)