A design framework for making algorithms supporting expert decisions more explainable

What is it about?

Algorithmic decision support systems are widely applied in domains ranging from healthcare to journalism. To ensure that these systems are fair and accountable, it is essential that humans can maintain meaningful agency, understand and oversee algorithmic processes. Explainability is often seen as a promising mechanism for enabling human-in-the-loop, however, current approaches are ineffective and can lead to various biases. We argue that explainability should be tailored to support naturalistic decision-making and sensemaking strategies employed by domain experts and novices.

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Photo by Carlynn Alarid on Unsplash

Photo by Carlynn Alarid on Unsplash