Monitoring AI Services for Misuse

Abstract

Given the surge in interest in AI, we now see the emergence of Artificial Intelligence as a Service (AIaaS). AIaaS entails service providers offering remote access to ML models and capabilities at arms-length', through networked APIs. Such services will grow in popularity, as they enable access to state-of-the-art ML capabilities, 'on demand', 'out of the box', at low cost and without requiring training data or ML expertise. However, there is much public concern regarding AI. AIaaS raises particular considerations, given there is much potential for such services to be used to underpin and drive problematic, inappropriate, undesirable, controversial, or possibly even illegal applications. A key way forward is through service providers monitoring their AI services to identify potential situations of problematic use. Towards this, we elaborate the potential for 'misuse indicators' as a mechanism for uncovering patterns of usage behaviour warranting consideration or further investigation. We introduce a taxonomy for describing these indicators and their contextual considerations, and use exemplars to demonstrate the feasibility analysing AIaaS usage to highlight situations of possible concern. We also seek to draw more attention to AI services and the issues they raise, given AIaaS' increasing prominence, and the general calls for the more responsible and accountable use of AI.