Clinicians don't know what explanations they need: A case study on eliciting AI software explainability requirements

This paper analyses how software developers elicit explainability requirements when creating a software application with an AI component, through a case study using AI in the medical context of predicting cerebral palsy (CP) risk in infants. Following a small software development team at a Norwegian hospital, we observe their process of simultaneously developing the AI application and discovering what explanations clinicians require from the AI predictions. Since clinicians struggled to articulate their explainability needs before interacting with the system, an iterative approach proved effective: the team started with minimal explanations and refined these based on clinicians' responses during real patient examinations. Our preliminary findings from the first two iterations show that clinicians valued "interrogative explanations" - i.e., tools that let them explore and compare the AI predictions with their own assessments - over detailed technical explanations of the AI model's inner workings. Based on our analysis, we suggest that successful explainability requirements emerge through iterative collaboration between developers and users rather than being fully specified upfront. To the best of our knowledge, this is the first empirical case study on eliciting explainability requirements in software engineering.