Ian René. Solano-Kamaiko
Cornell Tech, New York, New York, United States
AI technologies are increasingly deployed to support community health workers (CHWs) in high-stakes healthcare settings, from malnutrition diagnosis to diabetic retinopathy. Yet, little is known about how such technologies are understood by CHWs with low digital literacy and what can be done to make AI more understandable for them. This paper examines the potential of explorable explanations in improving AI understanding for CHWs in rural India. Explorable explanations integrate visual heuristics and written explanations to promote active learning. We conducted semi-structured interviews with CHWs who interacted with a design probe in which AI predictions of child malnutrition were accompanied by explorable explanations. Our findings show that explorable explanations shift CHWs' AI-related folk theories, help develop a more nuanced understanding of AI, augment CHWs' learning and occupational capabilities, and enhance their ability to contest AI decisions. We also uncover the effects of CHWs' sociopolitical environments on AI understanding and argue for a more holistic conception of AI explainability that goes beyond cognition and literacy.
doi.org/10.1145/3613904.3642733
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)