Purpose or Learning objective: This study reports on collaborative efforts within an AI lab developing solutions for lung cancer screening (LCS) to investigate and negotiate how fairness is framed and operationalized in practice, and to develop actionable recommendations for better algorithmic fairness practices in medical imaging AI. It contributes to calls for more context-sensitive, morally robust, and interdisciplinary approaches to algorithmic fairness.
Methods or Background: We draw on a two-year collaboration between engineers and an ethicist in a UMC AI lab, considering fieldnotes and observations from meetings, ethics roundtables, and a six-month project assessing the fairness of AI-based risk models for LCS (Sybil, Venkadesh21, PanCan2b). Leveraging “ethics parallel research” alongside elements of the JustEFAB framework, we reconstruct and critically examine practices, assumptions, and constraints shaping engineers’ approach. We trace how fairness was framed, bias assessed, and explore how fairness practices can be improved. Findings are validated in a follow-up roundtable, informing an agenda for more comprehensive, situated, and auditable fairness practices.
Results or Findings: We observe tensions between comprehensive theoretical frameworks of algorithmic fairness and narrow approaches prevalent in engineering practices, where fairness is often overlooked or reduced to technical optimization. Practical constraints (data availability, timelines, unclear responsibilities, limited resources and interdisciplinary opportunities) foster statistical approaches with limited connection to clinical needs or broader contextual ethical considerations. Bridging this gap requires reflexivity tools, transparent decision-making, interdisciplinary research, and recognition of fairness as a multifaceted ethical issue.
Conclusion: Current fairness assessments insufficiently engage with AI socio-technical and ethical dimensions. Interdisciplinary collaboration can bridge theory and practice, helping developers move beyond technical metrics. Ethics parallel research made these dimensions visible, contextual, and clinically relevant, while highlighting barriers and enablers of better fairness practices.
Limitations: Qualitative, single-center study focused on AI for medical imaging only.