Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Proceedings of the National Academy of Sciences of the United States of America
Measuring disparate impact in human and machine decisions.
Empirical analyses have grown increasingly important in discrimination litigation with the greater availability of detailed data on individuals and decisions. A popular analytic strategy is to estimate disparities after adjusting for observed covariates, typically with a regression model, in hopes of ferreting out discriminatory intent. This approach, however, is ill-suited to auditing algorithms that are now commonly used to aid decisions, which typically do not include race or other legally protected factors as inputs. Motivated by legal understandings of disparate impact, we introduce an approach that aims to measure "unjustified" disparities in both human and machine decisions. Our method, which we call risk-adjusted regression, proceeds in three steps. In the first step, we combine all available information in a machine learning model to estimate the value, or inversely, the risk, of taking a certain action, such as approving a loan application or hiring a job candidate. Second, we measure disparities in decisions after adjusting for these risk estimates alone. Finally, in the third step, we assess the sensitivity of results to potential mismeasurement of risk. We demonstrate this approach on a detailed dataset of 2.2 million police stops of pedestrians in New York City, and show that traditional statistical tests of discrimination can substantially understate the magnitude of (risk-adjusted) racial disparities.
28 July 2026
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Solidarity or Segregation? ChatGPT Health and US Health Care Disparities.
ChatGPT Health, an artificial intelligence feature launched by OpenAI in January 2026, integrates personal medical records and consumer health data into a large language model-based chatbot. It positions itself as a digital health tool to help users navigate a fragmented US health care system marked by inaccessible insurance, medical deserts, and workforce shortages. We argue that, rather than closing these gaps, ChatGPT Health risks widening what we call the solidarity gap in health care. By appearing to address unmet needs, it may obscure and entrench the structural conditions that produce health care disparities. We identify specific risks to patient safety, such as the legitimization of dangerous self-rationing and self-medication behaviors, inaccurate triage of clinical emergencies, erosion of effective health communication, and the reinforcement of confirmation and anchoring bias. These risks are particularly acute for individuals facing structural barriers to care. To prevent ChatGPT Health and similar large language model-based health tools from deepening inequities, we propose solidarity-based policy interventions such as upstream reforms that rebuild the institutional foundations of access and health equity and downstream safeguards that embed artificial intelligence tools within accountable, equitable health care infrastructures as complements to rather than substitutes for human care.
24 June 2026
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