Christina N. Harrington
Christina Harrington (she/her) is a designer and qualitative researcher who works at the intersection of interaction design and health and racial equity. She combines her background in electrical engineering and industrial design to focus on the areas of universal, accessible, and inclusive design. Specifically, she looks at how to use design in the development of products to support historically excluded groups such as Black and LatinX communities, older adults, and individuals with differing abilities in maintaining their health, wellness, and autonomy in defining their future. Christina is passionate about using design to center communities that have historically been at the margins of mainstream design. She looks to methods such as design justice and community collectivism to broaden and amplify participation in design by addressing the barriers that corporate approaches to design have placed on our ability to see design as a universal language of communication and knowledge.
Authored Publications
Sort By
Preview abstract
As artificial intelligence (AI) is rapidly integrated into healthcare, ensuring that this innovation helps to combat health inequities requires engaging marginalized communities in health AI futuring. However, little research has examined Black populations’ perspectives on the use of AI in health contexts, despite the widespread health inequities they experience–inequities that are already perpetuated by AI. Addressing this research gap, through qualitative workshops with 18 Black adults, we characterize participants’ cautious optimism for health AI addressing structural well-being barriers (e.g., by providing second opinions that introduce fairness into an unjust healthcare system), and their concerns that AI will worsen health inequities (e.g., through health AI biases they deemed inevitable and the problematic reality of having to trust healthcare providers to use AI equitably). We advance health AI research by articulating previously-unreported health AI perspectives from a population experiencing significant health inequities, and presenting key considerations for future work.
View details
Participatory AI Considerations for Advancing Racial Health Equity
Jatin Alla
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI) (2025) (to appear)
Toward Community- Led Evaluations of Text-to-Image AI Representations of Disability, Health, and Accessibility
Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) (2025)
Preview abstract
Responsible AI advocates for user evaluations, particularly when concerning people with disabilities, health conditions, and accessibility needs ( DHA)–wide- ranging but umbrellaed sociodemograph- ics. However, community- centered text- to- image AI’s ( T2I) evaluations are often researcher- led, situating evaluators as consumers. We instead recruited 21 people with diverse DHA to evaluate T2I by writing and editing their own T2I prompts with their preferred language and topics, in a method mirroring everyday use. We contribute user- generated terminology categories which inform future research and data collections, necessary for developing authentic scaled evaluations. We additionally surface yet- discussed DHA AI harms intersecting race and class, and participants shared harm impacts they experienced as image- creator evaluators. To this end, we demonstrate that prompt engineering– proposed as a misrepresentation mitigation– was largely ineffective at improving DHA representations. We discuss the importance of evaluator agency to increase ecological validity in community- centered evaluations, and opportunities to research iterative prompting as an evaluation technique.
View details
