Title:
Hearing artificial intelligence: Sonification guidelines & results from a case-study in melanoma diagnosis
Hearing artificial intelligence: Sonification guidelines & results from a case-study in melanoma diagnosis
Author(s)
R. Michael, Winters
Kalra, Ankur
Walker, Bruce N.
Kalra, Ankur
Walker, Bruce N.
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Abstract
The applications of artificial intelligence are becoming more and more prevalent in everyday life. Although many AI systems can operate autonomously, their goal is often assisting humans. Knowledge from the AI system must somehow be perceptualized. Towards this goal, we present a case-study in the application of data-driven non-speech audio for melanoma diagnosis. A physician photographs a suspicious skin lesion, triggering a sonification of the system's penultimate classification layer. We iterated on sonification strategies and coalesced around designs representing three general approaches. We tested each in a group of novice listeners (n=7) for mean sensitivity, specificity, and learning effects. The mean accuracy was greatest for a simple model, but a trained dermatologist preferred a perceptually compressed model of the full classification layer. We discovered that training the AI on sonifications from this model improved accuracy further. We argue for perceptual compression as a general technique and for a comprehensible number of simultaneous streams.
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Date Issued
2019-06
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Text
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Proceedings
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Licensed under Creative Commons Attribution Non-Commercial 4.0 International License.