This talk argues for a social ontological notion of contestability drawn from Critical Theory that is based on a mutual recognition of “standard authority”, i.e. the internalized dispositions of practice participants to mutually demand and give justification to each other.
Long abstract:
If “contestability” with respect to AI systems is supposed to mean more than formal mechanisms of voice and exit, or even redress, we need a better understanding of the concept of contest itself. Drawing on Critical Theory, in this talk I will argue that contestability is not only about providing those affected by AI systems with the formal means to contest. Also, meaningful contestability is based on a mutual recognition of “standard authority” (Stahl 2013), i.e. the internalized dispositions of practice participants to mutually demand and give justification to each other. As practices of datafication and prediction involving AI systems have not yet developed around such a notion of standard authority, I will distinguish different forms of (social) critique (reflective, therapeutical, and expressive critique) that can be employed to establish the mutual recognition relations necessary for contestability. Such a social ontological concept of contestability offers a theoretical grounding for voice, exit, and redress that is neither rooted in a discourse ethical ideal of equal expression of opinion, nor in an ideal of informed consent, as both of them would be too demanding for individuals as participants of everyday practices involving AI systems. Rather, it is based on a much less demanding idea of a mutual recognition of performances-as-interpretation within practices of contestation.