Ruth Schmidt

Zeya Chen, Zach Pino, Ruth Schmidt

Data donation, an emerging user-centric data collection method for public sector research, faces a gap between participant willingness and actual donation. This suggests a design absence in practice: while promoted as "donor-centered" with technical and regulational advances, a design perspective on how data choices are presented and intervene on individual behaviors remain underexplored. In this paper, we focus on pre-donation data exploration, a key stage for adequately and meaningful informed participation. Through a real-world data donation study (N=24), we evaluated three data exploration interventions (self-focused, social comparison, collective-only). Findings show choice framing impacts donation participation. The "social comparison" design (87.5%) outperformed the "self-focused view" (62.5%) while a "collective-only" frame (37.5%) backfired, causing "perspective confusion" and privacy concerns. This study demonstrates how strategic data framing addresses data donation as a behavioral challenge, revealing design's critical yet underexplored role in data donation for participatory public sector innovation.

Zeya Chen, Ruth Schmidt

Designing seamless, frictionless user experiences has long been a dominant trend in both applied behavioral science and artificial intelligence (AI), in which the goal of making desirable actions easy and efficient informs efforts to minimize friction in user experiences. However, in some settings, friction can be genuinely beneficial, such as the insertion of deliberate delays to increase reflection, preventing individuals from resorting to automatic or biased behaviors, and enhancing opportunities for unexpected discoveries. More recently, the popularization and availability of AI on a widespread scale has only increased the need to examine how friction can help or hinder users of AI; it also suggests a need to consider how positive friction can benefit AI practitioners, both during development processes (e.g., working with diverse teams) and to inform how AI is designed into offerings. This paper first proposes a "positive friction" model that can help characterize how friction is currently beneficial in user and developer experiences with AI, diagnose the potential need for friction where it may not yet exist in these contexts, and inform how positive friction can be used to generate solutions, especially as advances in AI continue to be progress and new opportunities emerge. It then explores this model in the context of AI users and developers by proposing the value of taking a hybrid "AI+human" lens, and concludes by suggesting questions for further exploration.