I study how different sociotechnical arrangements reconfigure what feels sensible to notice, care about, or act on in collective life. Be it the proactive assistance of smartphones or the comfort interiors of HVAC systems, how do these arrangements redraw the political sensible as they render certain forms of life somewhat natural, desirable, or at least bearable? At the same time, what issues and actions do these arrangements keep from taking hold—or otherwise render non-sensical?
Specifically, I approach these questions by attending to how different sociotechnical arrangements reconfigure the temporal and sensorial milieus of collective life. That is, I trace how such arrangements structure the conditions of perception and attunement, pattern the rhythms of habits and ruptures, and contour the horizons where certain stakes appear more urgent, timely, or foreseeable than others.
To do so, my work weaves together science and technology studies (STS), media studies, and critically oriented human-computer interaction (HCI). Ultimately, my goal is to show the political consequences of sociotechnical arrangements that are often taken for granted—both in terms of whose interests are truly advanced and what possibilities are quietly foreclosed. At the same time, I also hope to shed light on how different modes of living together might reopen the political sensible toward possibilities otherwise—even if they remain tenuous in the mean time.
Previous Projects
Before my PhD, I have studied participatory approaches to AI design and development across a variety of contexts, from architectural design to clinical decision-making.
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].
Documenting the Lived Experiences of Data Subjects
January 2020 - January 2021
Collaborator: Ranjit Singh, Amy Eng, Deana Gonzales, Ciarra Lee, Emma Li, Cassidy McGovern, Annika Pinch, Sterling Williams-Ceci, Kyra Wisniewski,, and Malte Ziewitz
Collaborator: Ranjit Singh, Amy Eng, Deana Gonzales, Ciarra Lee, Emma Li, Cassidy McGovern, Annika Pinch, Sterling Williams-Ceci, Kyra Wisniewski,, and Malte Ziewitz
Description:
Being ranked in web search engines can make or break or business, reputation, or career. Big companies and organizations may have the resources to hire PR and marketing consultants to affect their standing in the engines. But how do less well-resourced people make sense and cope with their predicament? Through a series of qualitative interviews, we collected the stories from small business owners and local activists to politicians running for public office and people re-entering society after prison terms.
Publication:
R. Singh, A., Eng, D. Gonzales, C. Lee, E. Li, C. McGovern, A. Pinch, S. Williams-Ceci, K., Wisniewski, S. Yang, and M. Ziewitz. (2020). Scoring Struggles: Everyday Experiences of Web Search Engines. Working Paper #1, August. Ithaca, NY: Digital Due Process Clinic.
