Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory. (arXiv:2205.05057v1 [cs.HC])

Web: http://arxiv.org/abs/2205.05057

Understanding how ML models work is a prerequisite for responsibly designing,
deploying, and using ML-based systems. With interpretability approaches, ML can
now offer explanations for its outputs to aid human understanding. Though these
approaches rely on guidelines for how humans explain things to each other, they
ultimately solve for improving the artifact -- an explanation. In this paper,
we propose an alternate framework for interpretability grounded in Weick's
sensemaking theory, which focuses on who the explanation is intended for.
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