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.

The authors would like to thank all the participants that took part in the crowdsourcing campaign. This research was partially funded by EIT Digital in the AIDE project (Activity Code: 19386).