Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"
It addresses fairness concerns in AIaaS for users and society, but is incremental as it builds on known bias issues in AI systems.
The paper investigates how the 'one-size-fits-all' approach of AI as a Service (AIaaS) can lead to biases and fairness issues due to its incompatibility with context-sensitive fairness, proposing a taxonomy of AI services based on user autonomy to systematize and highlight these challenges.
"AI as a Service" (AIaaS) is a rapidly growing market, offering various plug-and-play AI services and tools. AIaaS enables its customers (users) - who may lack the expertise, data, and/or resources to develop their own systems - to easily build and integrate AI capabilities into their applications. Yet, it is known that AI systems can encapsulate biases and inequalities that can have societal impact. This paper argues that the context-sensitive nature of fairness is often incompatible with AIaaS' 'one-size-fits-all' approach, leading to issues and tensions. Specifically, we review and systematise the AIaaS space by proposing a taxonomy of AI services based on the levels of autonomy afforded to the user. We then critically examine the different categories of AIaaS, outlining how these services can lead to biases or be otherwise harmful in the context of end-user applications. In doing so, we seek to draw research attention to the challenges of this emerging area.