As governments and institutions race to harness citizen data for the public good, design scholar and researcher Zeya Chen is asking the question no one else thought to ask: how to design for what data does to people？
Data donation is one of the more quietly radical ideas circulating in today’s AI landscape, where the demand for high-quality human data has never been greater. When governments and research institutions ask citizens to voluntarily share their personal data for the common good, they tend to frame it as either a regulatory challenge, questions of consent law and secondary data use, or a technical one, concerning storage, anonymization, and cybersecurity. What most of them have not seriously considered is that it might be, at its core, a design problem. Zeya Chen brings a human-centered design perspective that bridges behavioral insight with technical realities and lands in actual practice. That synthesis is precisely what has made Chen one of the most distinctive and sought-after voices at the intersection of behavioral design, data governance, AI ethics, and human-centered privacy.
A distinguished design researcher and practitioner, Chen holds an M.Des from the Institute of Design (ID) at Illinois Institute of Technology and a B.A. in Industrial Design from Wuhan University, China. Her doctoral research at the Institute of Design (ID), conducted in active collaboration with Northeastern University’s PEACH (Privacy-Enabling AI and Computer-Human Interaction) Lab, sits at the frontier of design and HCI. Her research has been published at the most selective venues in her field, including ACM CHI, CCS, the Design Research Society, HCII, and She Ji, the peer-reviewed journal of Design, Economics, and Innovation. She serves as expert reviewer and committee member for ACM CHI, DIS, CSCW, and TEI, holds Senior Membership in the Chongqing Engineers Association’s Industrial Design Committee in China, and is a Full Member of The Design Society in the UK. Beyond scholarship, her design practice has earned over 30 international awards, including multiple iF Design Awards, a Red Dot Award, Core77, FastCompany World-Changing Innovations, and Muse Design Awards, with work exhibited at internationally recognized venues including the Carrousel du Louvre in Paris, the Art Bethanien Biennial and Museum fur Kommunikation in Berlin, the Florence Annual International Art Exhibition, the Multicultural Art Festival in Melbourne, and the Bauhaus @ 85 Exhibition in Chicago.
That combination of scholarly depth and practitioner credibility is rarer than it sounds, and it is precisely what equips Chen to work on problems that neither the academy nor the industry has been able to solve alone. Most researchers lack the design fluency to translate findings into systems people can actually use. Most practitioners lack the behavioral rigor to know whether what they have built actually works. Chen brings both, and it shows in the work.
“Most researchers lack the design fluency to translate findings into systems people can use. Most practitioners lack the rigor to know whether what they built actually works. Chen brings both.”
Her most recent contribution was accepted to DRS 2026, the biennial conference of the Design Research Society in Edinburgh — the largest and most competitive gathering in design research history, with an acceptance rate of only 30 to 35 percent. The work makes the data donation argument empirically: through a real-world study, Chen evaluated three distinct ways of presenting data choices to participants before they decided whether to donate. A social comparison framing produced an 87.5% donation rate; a self-focused framing produced 62.5%; and a collective-only framing, which emphasized shared benefit without personalizing the decision, triggered privacy anxiety and caused donation to fall to 37.5%. The same data, the same stated purpose, the same civic goal, framed differently, produced outcomes that were not just different but opposite. It is a finding that becomes visible only when you bring a researcher’s empirical rigor together with a practitioner’s understanding of how designed experiences shape real behavior. Neither lens alone would have produced it.
Photo courtesy of Zeya
To understand why design framing carries such outsized influence on behavior, it helps to understand the intellectual framework underlying Chen’s work. The concept at its center is “positive friction”, and it runs directly counter to one of the technology industry’s most entrenched assumptions: that seamlessness is always the goal. Chen’s research presentation at the 2024 HCI International Conference in Washington, D.C. argues that in high-stakes contexts such as medical decisions, financial choices, data consent, and interactions with AI systems, deliberately designed friction and well-placed pauses can restore the cognitive space that frictionless interfaces systematically eliminate. Where a pure researcher might have proposed the concept as an abstract model, and a pure practitioner might have applied it intuitively without formalizing it, Chen did both: she introduced a behavioral model precise enough for researchers to test and extend, while grounding it in design knowledge that makes it immediately usable by practitioners. Positive friction is now a working principle in human-AI interaction because Chen gave it a form that speaks equally to both communities.
The data donation findings make immediate sense through this lens. A collective-only framing removes the personal, relational texture from the decision, eliminating the productive friction of self-reflection and replacing it with an abstraction too distant to feel real. Privacy anxiety fills the space where genuine consideration should have been. Design is never a neutral channel through which information passes. It is an active force shaping whether people think carefully or react defensively, and understanding that force requires exactly the kind of knowledge that lives at the intersection of behavioral science, technology, and design practice.
“Design is never a neutral channel through which information passes. It is an active force shaping whether people think carefully or react defensively.”
That intersection is equally visible in her privacy research. Most privacy-violating design choices are not malicious; they result from designers lacking the structured tools to recognize harm, especially when designing for users whose experiences differ from their own. Her work presented at the ACM Conference on Computer and Communications Security (CCS) introduced PrivacyMotiv, a system that uses large language models to help UX designers identify privacy harms and dark patterns before products ship. By integrating user personas, journey maps, and design audits into a unified pipeline, it makes harm visible at the design stage rather than after it has reached users. That a design research contribution earned recognition at one of the most selective cybersecurity conferences in the world reflects Chen’s intersectional positioning directly: scholar enough to publish at CCS, practitioner enough to build tools designers can actually use.
Photo courtesy of Zeya
At the broadest scale, her published research in She Ji proposes a Choice Triad Model for designers and policymakers working on complex public health challenges, drawing on the Flint water crisis and the U.S. COVID-19 vaccination rollout as grounding cases. The framework extends Chen’s behavioral design thinking beyond individual interfaces and into the domain of policy structure and civic infrastructure, offering tools for diagnosing how institutional systems shape human decision-making at a systemic level. It is scholarly work with direct policy application — perhaps the clearest expression of what her researcher-practitioner identity makes possible: research rigorous enough to appear in a leading Elsevier journal, and practical enough to reshape how public health designers and policymakers think about the conditions of human choice.
In a field that too often rewards depth in one domain at the expense of all others, Zeya Chen’s most significant contribution may be the demonstration that the most pressing problems in behavioral design, data governance, and human-centered AI cannot be solved from one side of the research-practice divide alone. The researchers who understand behavior do not always know how to design. The designers who know how to build do not always know how to measure. Chen has spent her career developing fluency in both, and directing that fluency at the problems that matter most. In a moment when questions of data, attention, privacy, and human agency have become genuinely urgent for everyone, that is not a niche expertise. It is exactly what the field needs.