Hauke Sandhaus will discuss "Bright Patterns: Towards Extra Ethical User Experience Design", and Ian René Solano-Kamaiko will present research on "Explorable Explainable AI: Improving AI Understanding for Community Health Workers in India."

"Bright Patterns: Towards Extra Ethical User Experience Design"

Dark Patterns have received significant attention from the academic Human-Computer Interaction community since 2010, when Brignull launched darkpatterns.org and initially described them as "tricks used in websites and apps that make you do things that you didn’t mean to." After years of collective action, governments worldwide are recognizing the need to regulate them—with the GDPR in the EU, the FTC in the U.S., and, more recently, the CCPA in India, all prohibiting specific types of Dark Patterns. Given the powerful influence that design has on user behavior, the time seems right to steer in the opposite direction. In this presentation, DLI Doctoral Fellow Hauke Sandhaus will introduce two ongoing projects: firstly, his definition of Bright Patterns as an antonym to Dark Patterns, and secondly, an empirical study examining how the evaluation standards of user experience designers do not necessarily guide them towards ethical design practices.

"Explorable Explainable AI: Improving AI Understanding for Community Health Workers in India"

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. DLI Doctoral Fellow Ian René Solano-Kamaiko will share his collaborative research, examining 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. Semi-structured interviews were conducted with CHWs who interacted with a design probe in which AI predictions of child malnutrition were accompanied by explorable explanations. 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. Solano-Kamaiko’s research also uncovers 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.