An Audit Framework for Adopting AI-Nudging on Children
This addresses ethical risks in AI-nudging for children, but it is incremental as it builds on existing audit concepts without introducing new technical methods.
The paper tackles the problem of ensuring ethical compliance in AI systems that use personalized, dynamic nudges on children by proposing an audit framework, which includes risk mitigation and reinforcement mechanisms for unintended consequences.
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