  AI is increasingly being offered 'as a service' (AIaaS). This entails service
providers offering customers access to pre-built AI models and services, for
tasks such as object recognition, text translation, text-to-voice conversion,
and facial recognition, to name a few. The offerings enable customers to easily
integrate a range of powerful AI-driven capabilities into their applications.
Customers access these models through the provider's APIs, sending particular
data to which models are applied, the results of which returned. However, there
are many situations in which the use of AI can be problematic. AIaaS services
typically represent generic functionality, available 'at a click'. Providers
may therefore, for reasons of reputation or responsibility, seek to ensure that
the AIaaS services they offer are being used by customers for 'appropriate'
purposes. This paper introduces and explores the concept whereby AIaaS
providers uncover situations of possible service misuse by their customers.
Illustrated through topical examples, we consider the technical usage patterns
that could signal situations warranting scrutiny, and raise some of the legal
and technical challenges of monitoring for misuse. In all, by introducing this
concept, we indicate a potential area for further inquiry from a range of
perspectives.
