Advancing Explainability through AI Literacy and Design Resources

Patrick Gage Kelley
Allison Woodruff
ACM Interactions, 30(5) (2023), pp. 34-38

Abstract

Explainability helps people understand and interact with the systems that make decisions and inferences about them. This should go beyond providing explanations at the moment of a decision; rather, explainability is best served when information about AI is incorporated into the entire user journey and AI literacy is built continuously throughout a person’s life. We share resources that encourage AI practitioners to think more broadly about what explanations can look like across their products and ways to provide people with a solid foundation that helps them better understand AI systems and decisions.