Conceptualising fairness: three pillars for medical algorithms and health equity

Abstract

ObjectivesFairness is a core concept meant to grapple with different forms of discrimination and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet, claims to fairness in ML discourses are often vague and contradictory. The response to these issues within the scientific community has been technocratic. Studies either measure (mathematically) competing definitions of fairness, and/or recommend a range of governance tools (eg, fairness checklists or guiding principles). To advance efforts to operationalise fairness in medicine, we synthesised a broad range of literature.MethodsWe conducted an environmental scan of English language literature on fairness from 1960-July 31, 2021. Electronic databases Medline, PubMed and Google Scholar were searched, supplemented by additional hand searches. Data from 213 selected publications were analysed using rapid framework analysis. Search and analysis were completed in two rounds: to explore previously identified issues (a priori), as well as those emerging from the analysis (de novo).ResultsOur synthesis identified ‘Three Pillars for Fairness’: transparency, impartiality and inclusion. We draw on these insights to propose a multidimensional conceptual framework to guide empirical research on the operationalisation of fairness in healthcare.DiscussionWe apply the conceptual framework generated by our synthesis to risk assessment in psychiatry as a case study. We argue that any claim to fairness must reflect critical assessment and ongoing social and political deliberation around these three pillars with a range of stakeholders, including patients.ConclusionWe conclude by outlining areas for further research that would bolster ongoing commitments to fairness and health equity in healthcare.

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

“…This was the strongest theme of the literature and conveyed manifold concerns about AI ethics and regulation ( Zelmer et al, 2018 ; Abdullah et al, 2021 ); ethical design and use of AI technologies in healthcare contexts ( Sikstrom et al, 2022 ); concerns about data privacy, data biases and data collection ( Harris, 2021 ; Ostherr, 2022 ); as well as concerns about trust, care quality, and liability ( Davenport and Kalakota, 2019 ; Sanal et al, 2019 ). There is a strong anticipation perspective relating to concerns about role replacement ( Johnston, 2018 ; Blease et al, 2019 ; Bridge and Bridge, 2019 ; Powell, 2019 ; Blease et al, 2020 ; Doraiswamy et al, 2020 ; Alrassi et al, 2021 ) and which parts of healthcare practice, can and should be entrusted to AI technologies ( Loftus et al, 2020 ; Nadin, 2020 ).…”

Algorithmic fairness in computational medicine

“…Fairness is not just the result of rigorous and thoughtful research, but rather the social and political processes needed to advance health equity. 92 With machine learning and artificial intelligence models gaining more attention, we should be aware of these issues when designing the models and appropriately mitigate them.…”

Algorithmic Fairness in Computational Medicine

“…Additionally, we introduced three types of bias mitigation methods, namely, pre-processing, in-processing and post-processing, and listed the popular software libraries and tools for bias evaluation and mitigation. Fairness is not just the result of rigorous and thoughtful research, but rather the social and political processes needed to advance health equity 99 . With machine learning and artificial intelligence models gaining more and more attentions, we should be aware of these issues when designing the models and appropriately mitigate them.…”