Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making.

Explainable Artificial Intelligence, or XAI, is a vibrant research topic in the artificial intelligence community. It is raising growing interest across methods and domains, especially those involving high-stakes decision-making, such as the biomedical sector. Much has been written about the subject, yet XAI still lacks shared terminology and a framework capable of providing structural soundness to explanations, a crucial need for decisions that impact healthcare. In our work, we address these issues by proposing a novel definition of explanation that synthesizes insights from the existing literature. We recognize that explanations are not atomic, but rather the combination of evidence stemming from the model and its input-output mapping, along with the human interpretation of this evidence. Furthermore, we fit explanations into the properties of faithfulness (i.e., the explanation is an accurate description of the model’s inner workings and decision-making process) and plausibility (i.e., how much the explanation seems convincing to the user). Our theoretical framework simplifies the operationalization of these properties and provides new insights into common explanation methods that we analyze through case studies. We explore the impact of our framework in the sensitive domain of biomedicine, where XAI can play a central role in generating trust by balancing faithfulness and plausibility.