About

Darren Gergle is a faculty member at Northwestern University, serving in the departments of Communication Studies and (by courtesy) Computer Science. His research focuses on Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW), and Computer-Mediated Communication (CMC). He is particularly interested in developing a theoretical understanding of the role that visual information plays in supporting communication and group interactions. A key component of his work involves applying social and cognitive psychology theory to the design, deployment, and evaluation of computing technologies.

Research topics

• Computer Science
• Psychology
• Sociology
• Human–computer interaction
• Machine Learning
• Multimedia
• Social Science
• Pedagogy
• Mathematics education
• Epistemology
• World Wide Web
• Management science
• Data science

Selected publications

• Speculative Fiction for Interdisciplinary, Proactive, and Publicly Engaged AI Ethics

  2026-04-13 · 1 citations

  article
  PublisherDOI

• Linguistic Similarity Within Centralized FLOSS Development

  2026-04-13

  articleOpen accessSenior author

  When free/libre and open source software (FLOSS) stewards centralize project development, they potentially undermine project sustainability and impact how contributors talk to each other. To study the relationship between steward-centralized development and contributor discussion, we compared the development of three Wikimedia platform features that the Wikimedia Foundation (WMF) built in MediaWiki. In a mixed-methods multi-case comparison, we used repository mining, linguistic style features, and principal component analysis to track MediaWiki feature development and issue discussions. Contrary to both our intuition and prior work, there were no identifiable differences in the linguistic style of WMF-affiliates and external contributors, even when feature development was guided by WMF contributions. From these results, we offer two provocations to the study of collaborative FLOSS development: (1) stewards dominate development according to their own use of specific project functionality; (2) centralized project development does not entail hierarchical language within project discussions.

  PublisherDOI

• "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models

  2026-04-13 · 1 citations

  articleOpen access

  Vision-Language Models (VLMs) are increasingly used by blind and low-vision (BLV) people to identify and understand products in their everyday lives, such as food, personal care items, and household goods. Despite their prevalence, we lack an empirical understanding of how common image quality issues—such as blur, misframing, and rotation—affect the accuracy of VLM-generated captions and whether the resulting captions meet BLV people’s information needs. Based on a survey of 86 BLV participants, we develop an annotated dataset of 1,859 product images from BLV people to systematically evaluate how image quality issues affect VLM-generated captions. While the best VLM achieves 98% accuracy on images with no quality issues, accuracy drops to 75% overall when quality issues are present, worsening considerably as issues compound. We discuss the need for model evaluations that center on disabled people’s experiences throughout the process and offer concrete recommendations for HCI and ML researchers to make VLMs more reliable for BLV people.

  PublisherDOI

• Algorithmic News Content Personalization and Readers’ Attitudes

  Digital Journalism · 2026-02-25 · 1 citations

  article
  PublisherDOI

• Access Is Not Enough: Toward Developmental Flourishing

  2026-04-13

  articleOpen accessSenior author

  “Access” framing in technical accessibility research often measures success primarily through task completion and outcome equivalence within existing visual-first activities. Such a focus overlooks process-oriented engagement that expands the capabilities of blind and low-vision (BLV) individuals in ways that are valuable and authentic to them. In this opinion paper, we illustrate how assistive technology can align with and augment processes unique to BLV individuals. We propose reframing accessibility around developmental flourishing. Flourishing positions assistive technologies not as end goals, but as means through which BLV people can meaningfully explore, create, make sense of their experiences, and continually redefine their engagement in activities. As our primary contribution, we distill the system-level paradigm shifts required to enable this transition: rethinking interfaces as cognitive representations, embodiment as communication, and adaptation as evolving-through-use systems. We conclude by discussing the benefits and challenges of this new frame and inviting ongoing conversation.

  PublisherDOI

• Replication materials for "Communication and Collaboration in Stewarded Open Source Software Development"

  Harvard Dataverse · 2026-04-23

  datasetOpen accessSenior author

  Replication materials for the DIIF report "Communication and Collaboration in Stewarded Open Source Software Development"

  PublisherDOI

• Linguistic Similarity Within Centralized FLOSS Development

  arXiv (Cornell University) · 2026-03-13

  preprintOpen accessSenior author

  When free/libre and open source software (FLOSS) stewards centralize project development, they potentially undermine project sustainability and impact how contributors talk to each other. To study the relationship between steward-centralized development and contributor discussion, we compared the development of three Wikimedia platform features that the Wikimedia Foundation (WMF) built in MediaWiki. In a mixed-methods multi-case comparison, we used repository mining, linguistic style features, and principal component analysis to track MediaWiki feature development and issue discussions. Contrary to both our intuition and prior work, there were no identifiable differences in the linguistic style of WMF-affiliates and external contributors, even when feature development was guided by WMF contributions. From these results, we offer two provocations to the study of collaborative FLOSS development: (1) stewards dominate development according to their own use of specific project functionality; (2) centralized project development does not entail hierarchical language within project discussions.

  PublisherDOI

• When Testing AI Tests Us: Safeguarding Mental Health on the Digital Frontlines

  ArXiv.org · 2025-04-29

  preprintOpen access

  Red-teaming is a core part of the infrastructure that ensures that AI models do not produce harmful content. Unlike past technologies, the black box nature of generative AI systems necessitates a uniquely interactional mode of testing, one in which individuals on red teams actively interact with the system, leveraging natural language to simulate malicious actors and solicit harmful outputs. This interactional labor done by red teams can result in mental health harms that are uniquely tied to the adversarial engagement strategies necessary to effectively red team. The importance of ensuring that generative AI models do not propagate societal or individual harm is widely recognized -- one less visible foundation of end-to-end AI safety is also the protection of the mental health and wellbeing of those who work to keep model outputs safe. In this paper, we argue that the unmet mental health needs of AI red-teamers is a critical workplace safety concern. Through analyzing the unique mental health impacts associated with the labor done by red teams, we propose potential individual and organizational strategies that could be used to meet these needs, and safeguard the mental health of red-teamers. We develop our proposed strategies through drawing parallels between common red-teaming practices and interactional labor common to other professions (including actors, mental health professionals, conflict photographers, and content moderators), describing how individuals and organizations within these professional spaces safeguard their mental health given similar psychological demands. Drawing on these protective practices, we describe how safeguards could be adapted for the distinct mental health challenges experienced by red teaming organizations as they mitigate emerging technological risks on the new digital frontlines.

  PublisherOA PDFDOI

• Beyond Accessibility: Understanding the Ease of Use and Impacts of Digital Collaboration Tools for Blind and Low Vision Workers

  2025-10-22 · 2 citations

  article
  PublisherDOI

• The Agony of Opacity: Foundations for Reflective Interpretability in AI-Mediated Mental Health Support

  ArXiv.org · 2025-12-18

  preprintOpen access

  Throughout history, a prevailing paradigm in mental healthcare has been one in which distressed people may receive treatment with little understanding around how their experience is perceived by their care provider, and in turn, the decisions made by their provider around how treatment will progress. Paralleling this offline model of care, people who seek mental health support from artificial intelligence (AI)-based chatbots are similarly provided little context for how their expressions of distress are processed by the model, and subsequently, any reasoning or theoretical grounding that may underlie model responses. People in severe distress who turn to AI chatbots for support thus find themselves caught between black boxes, contending with unique forms of agony that arise from these intersecting opacities. In this paper, we argue that the distinct psychological state of individuals experiencing severe mental distress uniquely necessitates a higher standard of end-user interpretability in comparison to general AI chatbot use. We propose a reflective interpretability approach to AI-mediated mental health support, which nudges users to engage in an agency-preserving and iterative process of reflection and interpretation of model outputs, towards creating meaning from interactions (rather than accepting outputs as directive instructions). Drawing on interpretability practices from four mental health fields (psychotherapy, crisis intervention, psychiatry, and care authorization), we describe concrete design approaches for reflective interpretability in AI-mediated mental health support, including role induction, prosocial advance directives, intervention titration, and well-defined mechanisms for recourse, alongside a discussion of potential risks and mitigation measures.

  PublisherOA PDFDOI

Recent grants

• CHS: Medium: Next Generation Content Production Tools for People with Vision Impairments

  NSF · $1.2M · 2019–2024

• CAREER: A Multi-Disciplinary Approach to the Next Generation of Collaborative Technologies

  NSF · $502k · 2010–2015

• CHS: Small: Collaborative Research: Structured Data Peer Production: Addressing Challenges and Leveraging Opportunities

  NSF · $250k · 2018–2025

Frequent coauthors

• Tom Brinck

  19 shared

• Scott D. Wood

  Knowledge Based Systems (United States)

  17 shared

• Brent Hecht

  Northwestern University

  15 shared

• Haoqi Zhang

  11 shared

• Robert E. Kraut

  11 shared

• Sarah D’Angelo

  9 shared

• Alastair J. Gill

  Devon Partnership NHS Trust

  8 shared

• Anne Marie Piper

  University of California, Irvine

  8 shared

Education

• PhD, Human Computer Interaction Institute

  Carnegie Mellon University

  2006

• MSI, School of Information

  University of Michigan

  2001