Promoting Fairness in Classification of Quality of Medical Evidence (2023)

Abstract: Automatically rating the quality of published research is a critical step in medical evidence synthesis. While several methods have been proposed, their algorithmic fairness has been overlooked even though significant risks may follow when such systems are deployed in biomedical contexts. In this work, we study fairness on two systems along two sensitive attributes, participant sex and medical area. In some cases, we find important inequalities, leading us to apply various debiasing methods. Upon examining an interplay of systems’ predictive performance, fairness, as well as medically critical selective classification capabilities and calibration performance, we find that fairness can sometimes improve through debiasing, but at a cost in other performance measures.

## introduction automated methods for quality assessment of medical evidence have been developed to assist human experts in rating the quality of design, conduct, and reporting of published medical research. this includes predicting whether a study is affected by bias along several dimensions, or how strong the evidence is for a body of medical evidence constrained by a clinical question. a number of studies have proposed techniques and datasets for automated quality assessment @xcite @xcite , as well as follow-up research on practicality, expert acceptability, and reliability of these approaches @xcite @xcite @xcite @xcite . recent research has shown that machine learning (ml) techniques may suffer from bias when making decisions for people in different subgroups, which can lead to detrimental effects on the health and well-being of disadvantaged and underrepresented populations @xcite . 1 how to assess and mitigate such bias has been a topic of ongoing research in broader ml and natural language processing (nlp) contexts @xcite , including in the biomedical domain @xcite . however, there has been a lack of research specifically addressing fairness and bias mitigation in automated quality assessment of medical evidence, despite unfair algorithmic decisions potentially having a large impact on either promoting or thwarting access to quality research for individual groups. a biased quality assessment classifier may systematically favour or discriminate against research conducted on participants of a specific sex or within a particular medical area. it could, for example, tend to systematically miss higherquality evidence for urology while working better for cardiology. 2 by extension, the findings from studies conducted on a specific population (e.g. from urology) or within a particular area would be undervalued or overlooked, and as a result, medical evidence relevant to those patients may not be recognized as such. an important reason for variable performance across across medical areas is the varying availability of medical evidence in the first place as well as the prevalence of higherquality evidence @xcite . these may be grounded in different research practices and approaches to scientific assessment of interventions that have become established in medical fields @xcite . the consequences of such performance disparities and inequalities in the availability of medical evidence can be far-reaching, leading to outdated, ineffective, or even incorrect treatment recommendations. we aim in this paper to: while fairness can be understood in a number of ways @xcite , we take it to mean that all protected groups should have the same likelihood of being classified favourably @xcite . that is, regardless of participant sex in a study or medical area within which a clinician works, the system should be equally likely to categorise that evidence as higher-quality. we apply bias mitigation techniques to either manipulate the data or the learning mechanism in an attempt to increase fairness. we believe that investigating fairness in the context of quality classification of medical evidence can lead to more transparency, as well as raised awareness of potentially disparate outcomes on subgroups to which classifiers are applied. ## models and data we work with two systems that differ in their intended use. evidencegrader rates the overall quality of a group of related studies @xcite , whereas trialstream-errob @xcite focuses on overall risk of bias (rob) in a single clinical study. next, we describe those two systems in more detail. ## results the results for evidencegrader and trialstream-errob with different protected attributes are shown in tables 1 to 4 . looking at the models without debiasing first ("vanilla"), the predictive performance is somewhat higher for evidencegrader (around .71 f1) than trialstreamerrob (.66 f1). 10 reasons for this are likely varied but could include the fact that inputs to trialstreamerrob are abstracts only in our setting, while the overall rob may only be discernible from finer-grained judgments that require access to full texts. the models otherwise perform similarly in terms of selective classification and calibration. the non-enhanced fairness of the vanilla models is highest for evidencegrader+sex, where we find only small differences in tpr between groups (figure 1 ). for trialstreamerrob+sex, as well as for both models with the area attribute (figure 2 ), (2), and s is the total number of hyperparameter settings for different techniques (43). for evidencegrader, we tune hyperparameters only on the development set of the first fold of cross-validation, and use the best setting for the remaining folds. 10 marshall et al. ( 2020 ) report an f1∼0.5 for rob assessment in trialstreamer. @xcite , whose approach is markedly different from ours (e.g. their model predicts individual criteria rather than overall risk), report auc (∼0.69) instead of f1. ## conclusion and future work we showed in this work that data rebalancing and training-based debiasing methods can sometimes improve fairness of quality assessment classifiers using sex or medical area as protected attributes. however, as this usually comes at the expense of predictive and selective classification performance, the decision about whether to mitigate bias should lie with domain experts who can consider the relative importance of different performance aspects. in future work, we plan to consider using different weights for the contribution of different model selection criteria, or imposing hard (minimal) constraints on specific criteria (e.g. tpr of individual groups). better understanding of this would enable practitioners to "interact" with different aspects of performance in a more nuanced way.