I study the politics of technology, society, and (built) environments. My research traces the sociotechnical arrangements that (re)organize the milieus we inhabit, with an emphasis on how they rework what is noticed and acted on—and what is assumed, normalized, or backgrounded as conditions. My research is broadly situated in critical and interpretative modes of inquiry. Specifically, I draw on theories and approaches across materialist media studies, science & technology studies, infrastructure studies, and human geography.
My recent projects focus on historical and contemporary cases where techniques of sensing and prediction reconfigure the milieus we inhabit. My ongoing dissertation work turns to the case of building management systems (BMS) in office buildings. Zooming in on the tensions between comfort and energy efficiency, I trace how the governance of energy, labor, and real estate development is fastened together in and around these systems. Aside from my dissertation work, I've also studied the discursive invocation of multi-planetary future by the NewSpace industry, the Doomsday Clock as a securitization device against existential threats, as well as speculating (other) worlds beyond the predictive paradigm of AI.
Aside from my primary research program, I also take up critical design to interrogate how alternative worlds could be made possible. With interdisciplinary researchers and community partners, I’ve worked with youth of color in Los Angeles to reimagine environmental futures amidst anthropogenic changes, and I’ve engaged with Israeli and Palestinian residents to build communal spaces in borderlands shaped by uneven geopolitics.
Previous Projects
Before my PhD, I've worked on a range of projects across participatory design, human-computer interaction (HCI), and digital cultures:
Mapping the Participatory Turn in AI Design
March 2021 - September 2023
Collaborator: Fernando Delgado, Michael Madaio, Qian Yang
Collaborator: Fernando Delgado, Michael Madaio, Qian Yang
Description:
Despite the growing consensus that stakeholders affected by AI systems should participate in their design, enormous variation and implicit disagreements exist among current approaches. For researchers and practitioners who are interested in taking a participatory approach to AI design and development, it remains challenging to assess the extent to which any participatory approach grants substantive agency to stakeholders.
Methods:
Stage 1 - Conceptual Framework: We first derive a conceptual framework through synthesis of literature across technology design, political theory, and the social sciences that researchers and practitioners can leverage to evaluate approaches to participation in AI design.
Stage 2 - Empirical Foundation: We then evaluated the current state of stakeholder participation in AI. We developed a corpus including 80 research publications that described AI projects in which stakeholder participation was pursued, as well as 12 interviews with researchers and industry practitioners who were authors on these papers.
Stage 2 - Empirical Foundation: We then evaluated the current state of stakeholder participation in AI. We developed a corpus including 80 research publications that described AI projects in which stakeholder participation was pursued, as well as 12 interviews with researchers and industry practitioners who were authors on these papers.
Publication:
Fernando Delgado*, Stephen Yang*, Michael Madaio†, and Qian Yang†. (2023). The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice. Paper accepted to the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO'23). [full paper]. [video]. [summary].
Fernando Delgado, Stephen Yang, Michael Madaio, and Qian Yang. (2021). Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir." Paper Presented at the Human-Centered AI Workshop at NeurIPS 2021. [link].
* Co-first authors and co-last authors contributed equally.
Fernando Delgado, Stephen Yang, Michael Madaio, and Qian Yang. (2021). Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir." Paper Presented at the Human-Centered AI Workshop at NeurIPS 2021. [link].
* Co-first authors and co-last authors contributed equally.
When Design Workshops Meet Chatbot: Operationalizing Participatory Architectural Design
January 2022 - August 2022
Collaborator: Jonathan Dortheimer, Aaron Sprecher, Qian Yang
Collaborator: Jonathan Dortheimer, Aaron Sprecher, Qian Yang
Description:
Today’s participatory design (PD) processes focus on face-to-face design workshops as the primary method of participation. However, their time- and resource-intensive nature poses practical constraints on the extent to which these workshops can amplify diverse voices. With the maturing capabilities of large language models (LMs) like GPT-3, chatbots present great promise as an emerging PD method that can distribute participation across geographic, resource, and language differences. To this end, we facilitated participatory architectural design project in northern Israel through a hybrid PD workflow that leveraged both (1) group-based, face-to-face design workshops and (2) one-on-one, remote, GPT-3-powered chatbot engagement. Our experience offers a valuable reference for future research that seeks to incorporate chatbots in PD processes or explore spatial-temporal possibilities of PD beyond traditional face-to-face workshops.
Publication:
Stephen Yang, Jonathan Dortheimer, Qian Yang, Aaron Sprecher. (2024). When Design Workshops Meet Chatbots: Meaningful Participation at Scale?. International Journal of Architectural Computing. [link].
Jonathan Dortheimer, Stephen Yang, Qian Yang, Aaron Sprecher. (2023). Conceptual Architectural Design at Scale: A Case Study of Community Participation Using Crowdsourcing. Buildings, 13, 222. [link].
Jonathan Dortheimer, Stephen Yang, Qian Yang, Aaron Sprecher. (2023). Conceptual Architectural Design at Scale: A Case Study of Community Participation Using Crowdsourcing. Buildings, 13, 222. [link].
Harnessing Biomedical Literature to Calibrate Clinicians' Trust in AI-Powered Decision Support Systems
January 2021 - January 2023
Collaborator: Yiran Zhao, Qian Yang, Yuexing Hao, Kexin Quan, Fei Wang, Volodymyr Kuleshov, Bojian Hou
Collaborator: Yiran Zhao, Qian Yang, Yuexing Hao, Kexin Quan, Fei Wang, Volodymyr Kuleshov, Bojian Hou
Description:
Clinical decision support tools (DSTs), powered by Artificial Intelligence (AI), promise to improve clinicians' diagnosis and treatment decision-making. However, no AI model is always correct. DSTs must enable clinicians to validate each AI suggestion, convincing them to take correct suggestions while rejecting errors. Towards this goal, existing DST designs often explain AI's inner workings or performance indicators. We chose a different approach: We investigated how clinicians validated each other's suggestions in practice.
Methods:
As a first step, we conducted 12 interview with clinicians to investigate how clinicians sought, prioritized, and synthesized information from the literature to validate care suggestions. We paid particular attention to whether and how clinicians' behaviors deviated from best practices in the time pressure of patient care.
Prototype
Building on our insights, we designed a new DST that embraces clinicians' naturalistic evidence-calibration behaviors. The web-based prototype can be accessed here. This design helps clinicians deliberate whether to take an AI's suggestion by providing a list of supporting and opposing evidence from biomedical literature.
Publication
Qian Yang, Yuexing Hao*, Kexin Quan*, Stephen Yang*, Yiran Zhao*, Volodymyr Kuleshov, and Fei Wang. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 ACM Conference on Human Factors in Computing Systems (CHI’23). [link].
* Co-second authors contributed equally listed in alphabetical order.
* Co-second authors contributed equally listed in alphabetical order.
Playing with Visibility in Underground Electronic/Dance Music Scenes
March 2021 - January 2023
Description:
The mediation of mobile and social media has reshaped how people imagine, understand, and, in turn, negotiate the visibility of their self-expression, information sharing, and relationship-building. Prior work on visibility management has focused on the online circulation of content without attending to the spatial-temporal context of situated technology use. To investigate these dimensions of visibility management on and through mobile and social media, I examined how participants in underground electronic/dance music culture (EDMC) manage their visibility. This research examines how the mediation of mobile and social media has reshaped what participants of underground EDMC do to maintain their shared culture of secrecy.
Methods
I conducted 20 nights of field observations at live music events and 27 semi-structured interviews with scene participants. I adopted a multi-site approach by studying four underground dance music scenes in distinct socio-political contexts––New York City; Ithaca, New York; Taipei, Taiwan; Berlin, Germany. Taking a “field site as network” approach (Burrell, 2009), I selected the four music scenes as “entry points” to gain access to underground EDMC.
Publications:
Stephen Yang. (2024). Playing with Visibility: Underground Electronic/Dance Music in the Smartphone Age. International Journal of Communication. [link].