Connecting algorithmic fairness and fair outcomes in a sociotechnical simulation case study of AI-assisted healthcare
Bibliographic record
Abstract
Artificial intelligence (AI) has vast potential for improving healthcare delivery, but concerns regarding biases in these systems have raised important questions regarding fairness when deployed clinically. Most prior studies on fairness in clinical AI focus solely on performance disparities between subpopulations, which often fall short of connecting the technical outputs of AI systems with sociotechnical outcomes. In this work, we present a simulation-based approach to explore how statistical definitions of algorithmic fairness translate to fairness in long-term outcomes, using AI-assisted breast cancer screening as a case example. We evaluate four fairness criteria and their impact on mortality rates and socioeconomic disparities, while also considering how clinical decision makers’ reliance on AI and patients’ access to healthcare affect outcomes. Our results highlight how algorithmic fairness does not directly translate into fair and equitable outcomes, underscoring the importance of integrating sociotechnical perspectives to gain a holistic understanding of fairness in healthcare AI. Artificial intelligence (AI) can greatly improve healthcare delivery and outcomes, but potential embedded biases can affect fairness in clinical deployment. Here, the authors develop a simulation-based approach to explore which formalisations of AI algorithmic fairness translate into long-term outcome fairness, with a focus on breast cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".