Date of Award
12-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Human Centered Computing
Committee Chair/Advisor
Dr. Guo Freeman
Committee Member
Dr. Nathan McNeese
Committee Member
Dr. Bart Knijenburg
Committee Member
Dr. Emma Dixon
Abstract
Rapid advancements in the technical capabilities and availability of generative Artificial Intelligence (AI) systems, such as OpenAI's ChatGPT, have drawn widespread attention to the opportunities and challenges associated with AI integration into everyday workplaces (i.e., office-type work). Following calls for organizations to consider the ethical and workplace-specific impacts of generative AI's use before integrating it into the workplace, this dissertation addresses three critical gaps in Human-Centered Computing (HCC) and AI workplace integration research. First, this dissertation unpacks the underdeveloped links between women's representation - or lack thereof - in AI-related fields and how their experiences with gendered workplace dynamics in said fields affects their decision-making when designing and evaluating AI workplace integration. Second, this dissertation directly addresses the critical need to clarify further how the introduction of AI into everyday workplaces will impact the ways through which other people within the everyday workplace will evaluate and attribute credit to the human workers who work with the AI, especially considering the differing impacts that workplace gender dynamics can have on women workers. Third, this dissertation ultimately aims to understand how the design and evaluation of human-AI collaborative integration into everyday workplaces might contribute to perceptions of human worker job security and whether workplace gender dynamics impact these understandings.
In doing so, Study 1 constitutes the ``design'' portion of this dissertation's larger narrative and addresses the aforementioned gaps via eight in-depth, qualitative interviews with diverse women who work in AI design, development, and research. These interviews focused on understanding how these women perceive the role that their gender identity plays in their experiences within the male-dominated AI industry, and how said perceptions of their workplaces' gender dynamics influence their approaches to designing and developing AI. Study 1's findings provide one of the only empirical investigations linking the enduring underrepresentation of women in AI development to the reinforcement of gendered assumptions in the design and development of AI, and how these women subsequently challenge these assumptions in AI development by advocating for use-case specificity and intersectionality in development practices.
Study 2 constitutes the ``evaluation" portion of this dissertation's larger narrative, focusing on empirically investigating how evaluations and attributions of credit in the workplace are influenced by technological and gender dynamics within everyday workplaces. To do this, Study 2 conducted a large 2x4x2 online factorial survey experiment with 685 participants that explored how the gender presentation of a human worker (i.e., woman v. man), the gender or AI functional design of said worker's collaborative partner (i.e., an AI tool v. an AI teammate v. a human female teammate v. a human male teammate), and the type of task completed by the partnership (i.e., meeting scheduling v. investment banking) all contribute to the evaluation of and attributions of credit to workers and their collaborative partners in AI-enabled workplaces. Study 2 then leveraged interviews with six experts in people management in AI-enabled workplaces to gain real-world insights on these results. Study 2 thus informs expert-crafted recommendations for how workplaces looking to integrate AI into their practices can best design AI to work collaboratively with humans as teammates. These insights strike a balance between gaining the benefits of AI use without inadvertently disadvantaging and disenfranchising human workers, especially women workers.
Finally, Study 3 offers a deeper examination of the pathways that lead from dyadic composition in AI-enabled workplaces to human worker job security, with the ultimate goal of providing workers, particularly women, with new insights into how AI workplace integration may impact their job security. Built upon data collected during Study 2, Study 3 conducted a more comprehensive analysis using structural equation modeling. Similar to Study 2, Study 3 then leveraged interviews with six experts in people management in AI-enabled workplaces to gain real-world insights. Study 3 thus provides comprehensive insights into how workplace gender dynamics, AI workplace technology, individual differences, partnership evaluations, and attributions of credit interact to influence the job security of future workers in AI-enabled workplaces. Such insights highlight how the revealed paths can inform expert-crafted recommendations for helping workplaces best integrate AI into their workflows while still accounting for the well-being and sense of security of their human workforce.
Overall, the three studies of this dissertation stand to make significant contributes towards research and ongoing global conversations in HCC and Human-Centered AI research about AI’s impacts on the working lives of women as well as gendered aspects of AI design. It does so by explicitly considering how workplace gender and technological dynamics shape the design and evaluation of AI integration into everyday workplaces, how these dynamics intermingle with AI functional design to impact evaluations of and attributions of credit to women workers, and how these dynamics and evaluations lead to perceptions of human worker job security in AI-enabled workplaces to impact women workers specifically. As such, this dissertation provides much-needed insight into current and future efforts to integrate generative AI into everyday workplaces in light of ongoing ambiguity and discourse surrounding the ethical and causal attributional challenges that AI workplace integration presents.
Recommended Citation
Schulenberg, Kelsea S., "AI In Consideration of Her: Accounting for Gendered Workplace Dynamics in the Design and Evaluation of Human-Centered AI Integration in Everyday Workplaces" (2025). All Dissertations. 4155.
https://open.clemson.edu/all_dissertations/4155
Author ORCID Identifier
https://orcid.org/0000-0002-1503-7872