Explainable artificial intelligence for mental health through transparency and interpretability for understandability.

ABSTRACT: The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what "explainability" means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability meaning model-agnostic techniques that augment a complex model (with internal mechanics intractable for human understanding) with a simpler model argued to deliver results that humans can comprehend. Given the differing usage and intended meaning of the term "explainability" in AI and ML, we propose instead to approximate model/algorithm explainability by understandability defined as a function of transparency and interpretability. These concepts are easier to articulate, to "ground" in our understanding of how algorithms and models operat

SUBMITTER: Joyce DW 

PROVIDER: S-EPMC9849399 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature