± HEALTH For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep le…± LIFESTYLE Loneliness is a pressing global health issue, yet traditional interventions often fall short due to scalabili…± POLICY Abstract As AI systems increasingly influence everyday life, integrating diverse community values is both eth…+ HEALTH Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resoluti…+ POLICY As generative AI (GAI) becomes increasingly embedded in higher education, universities worldwide are developi…+ HEALTH ABSTRACT Efficient, accurate phenotyping for antidepressant treatment response in electronic health records (…+ HEALTH Electronic health records (EHRs) offer considerable potential for clinical prediction, but their complexity a…− HEALTH Abstract Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzi…
TruaceTracing the truth around AIFriday, July 17, 2026
Subscribe
TRV-2026-0253Certified recordPeer-reviewed

Co-designing AI systems with value-sensitive citizen science

Abstract As AI systems increasingly influence everyday life, integrating diverse community values is both ethically essential and practically urgent. This paper presents Value-Sensitive Citizen Science (VSCS)—a systematic framework that combines Value-Sensitive Design (VSD) with citizen science to support meaningful public participation in AI development. VSCS addresses gaps in the existing approaches by integrating culturally grounded methods and cognitive scaffolding through the Participatory Value-Cognition T…

Policy · The Trace — both readings · certified 2026-07-17 · v1 · article view · machine-readable

Current reading — gain

VSCS framework enables community members to act as co-researchers and translate local values into technical requirements for AI systems.

Current reading — problem

Existing AI development remains monocultural and top-down, with gaps in inclusion and persistent power asymmetries and epistemic justice concerns.

What this doesn’t fix

Framework faces unresolved challenges around power asymmetries, scalability, and epistemic justice across diverse sociotechnical contexts.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-0253, v1: “Co-designing AI systems with value-sensitive citizen science.” Truvace, 2026-07-17. /record/TRV-2026-0253 (accessed at citation time). sha256 6080c6e5800dedc4…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv16080c6e5800d…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-0253 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.