Sheena Erete
Associate Professor, College of Information
University of Maryland
Sheena Erete is an associate professor in the College of Information at the University of Maryland and the Associate Director of Research for the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM). Her research focuses on co-designing socio-cultural technologies, practices and policies with community residents to amplify their local efforts. Erete's work has addressed topics such as community safety, education, civic engagement, and health.
Areas of Expertise: Community-Centered AI Design
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Alisha Pradhan, Sheena Erete, Shaan Chopra, Pooja Upadhyay, Oluwaseun Sule, and Amanda Lazar. 2025. 'No, not that voice again!': Engaging Older Adults in Design of Anthropomorphic Voice Assistants. Proc. ACM Hum.-Comput. Interact. 9, 2, Article CSCW141 (May 2025), 30 pages.
Abstract: Conversational voice assistants are often imbued with personality and human-like characteristics (e.g., gender). While researchers have begun to examine and design for the downstream societal impacts of voice assistants encoding characteristics such as gender, we know little about other human-like characteristics such as age that are encoded in an artificial, yet, anthropomorphic voice. As older adults continue to adopt voice assistants, we brought older adults into an activity to customize human-like characteristics for their voice assistant. Our findings reveal the different stereotypes and assumptions individuals associated with voice assistant characteristics (e.g., age, gender, race). We also describe individuals' motivations behind customizing or not customizing these characteristics. We discuss how biases get encoded through our design process, marginalizing older adults and other non-dominant user groups and call for a need to examine the systemic, yet unspoken, power structures encoded in anthropomorphic technologies.
Full Paper -
Jessa Dickinson, Natasha Smith-Walker, Burrell Poe, Bradly K Johnson, Sharif Walker, and Sheena Erete. 2025. Community-Driven Data Analysis: Advancing Methods to Achieve Community Goals in Collaborative Research. Proc. ACM Hum.-Comput. Interact. 9, 2, Article CSCW050 (May 2025), 33 pages.
Abstract: There has been growing attention in HCI to the potential for community-based participatory research (CBPR) to cause harm to community partners. Extractive research is when researchers take ''data'' (i.e., stories, knowledge) and other resources (e.g., time, labor) from communities but provide little in return. Scholars have examined collaboration practices, but this work has yet to focus on data analysis. We (academic and community researchers) explore the benefits, challenges, and power dynamics involved in collaborative analysis. We reflected on our process to co-analyze workshop data from a community-led initiative through member-checking interviews and a duo ethnography. In this paper, we detail the co-analysis approach we used and examine how structural power can incentivize extractive research practices. We pose that co-analyzing data according to community-defined questions can mitigate harm and advance community partners' goals.
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Devansh Saxena, Zoe Kahn, Erina Seh-Young Moon, Lauren Marietta Chambers, Corey Jackson, Min Kyung Lee, Motahhare Eslami, Shion Guha, Sheena Erete, Lilly Irani, Deirdre Mulligan, and John Zimmerman. 2025. Emerging Practices in Participatory AI Design in Public Sector Innovation. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '25). Association for Computing Machinery, New York, NY, USA, Article 777, 1–7.
Abstract: Local and federal agencies are rapidly adopting AI systems to augment or automate critical decisions, efficiently use resources, and improve public service delivery. AI systems are being used to support tasks associated with urban planning, security, surveillance, energy and critical infrastructure, and support decisions that directly affect citizens and their ability to access essential services. Local governments act as the governance tier closest to citizens and must play a critical role in upholding democratic values and building community trust especially as it relates to smart city initiatives that seek to transform public services through the adoption of AI. Community-centered and participatory approaches have been central for ensuring the appropriate adoption of technology; however, AI innovation introduces new challenges in this context because participatory AI design methods require more robust formulation and face higher standards for implementation in the public sector compared to the private sector. This requires us to reassess traditional methods used in this space as well as develop new resources and methods. This workshop will explore emerging practices in participatory algorithm design – or the use of public participation and community engagement - in the scoping, design, adoption, and implementation of public sector algorithms.