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Understanding how ML models work is a prerequisite for responsibly designing,deploying, and using ML-based systems. With interpretability approaches, ML cannow offer explanations for its outputs to aid human understanding. Though theseapproaches rely on guidelines for how humans explain things to each other, theyultimately solve for improving the artifact -- an explanation. In this paper,we propose an alternate framework for interpretability grounded in Weick'ssensemaking theory, which focuses on who the explanation is intended for.Recent work has advocated for the importance of understanding stakeholders'needs -- we build on this by providing concrete properties (e.g., identity,social context, environmental cues, etc.) that shape human understanding. Weuse an application of sensemaking in organizations as a template for discussingdesign guidelines for Sensible AI, AI that factors in the nuances of humancognition when trying to explain itself.

Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory