  This is an audit framework for AI-nudging. Unlike the static form of nudging
usually discussed in the literature, we focus here on a type of nudging that
uses large amounts of data to provide personalized, dynamic feedback and
interfaces. We call this AI-nudging (Lanzing, 2019, p. 549; Yeung, 2017). The
ultimate goal of the audit outlined here is to ensure that an AI system that
uses nudges will maintain a level of moral inertia and neutrality by complying
with the recommendations, requirements, or suggestions of the audit (in other
words, the criteria of the audit). In the case of unintended negative
consequences, the audit suggests risk mitigation mechanisms that can be put in
place. In the case of unintended positive consequences, it suggests some
reinforcement mechanisms. Sponsored by the IBM-Notre Dame Tech Ethics Lab
