AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications

More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities
to provide advanced features; on the other hand, human-in-the-loop approaches are on the
rise to involve people in AI-powered pipelines for data collection, results validation and decision making.
Does the introduction of AI features affect user acceptance? Does the AI result quality
affect people’s willingness to use such applications? Does the additional user effort required in
human-in-the-loop mechanisms change the application adoption and use?
This study aims to provide a reference approach to answer those questions. We propose a model
that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to
AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and
collaborative intention – willingness to contribute to AI pipelines.
We tested the proposed model with an application for car damage claim reporting with AI-powered
damage estimation for insurance customers. The results showed that the XAI related factors have
a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the
application. Moreover, there is a strong link between behavioral intention and collaborative intention,
indicating that indeed human-in-the-loop approaches can be successfully adopted in final user
applications.

Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:

• FlawlessAI-Group prototype

• FailingAI-Group prototype

This study is shared as a research object adopting the RO-Crate specification.