Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLP (2020)

Abstract: Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as free text. We propose a novel task of exploring fairness on a multimodal clinical dataset, adopting equalized odds for the downstream medical prediction tasks. To this end, we investigate a modality-agnostic fairness algorithm - equalized odds post processing - and compare it to a text-specific fairness algorithm: debiased clinical word embeddings. Despite the fact that debiased word embeddings do not explicitly address equalized odds of protected groups, we show that a text-specific approach to fairness may simultaneously achieve a good balance of performance classical notions of fairness. Our work opens the door for future work at the critical intersection of clinical NLP and fairness.

## introduction natural language processing is increasingly leveraged in sensitive domains like healthcare. for such critical tasks, the need to prevent discrimination and bias is imperative. indeed, ensuring equality of health outcomes across different groups has long been a guiding principle of modern health care systems @xcite . moreover, medical data presents a unique opportunity to work with different modalities, specifically text (e.g., patient narratives, admission notes, and discharge summaries) and numerical or categorical data (often denoted tabular data, e.g., clinical measurements such as blood pressure, weight, or demographic information like ethnicity). multi-modal data is not only reflective of many real-world settings, but machine learning models which leverage both structured and unstructured data often achieve greater performance than their individual constituents @xcite . while prior work studied fairness in the text and tabular modalities in isolation, there is little work on applying notions of algorithmic fairness in the broader multimodal setting @xcite . our work brings a novel perspective towards studying fairness algorithms for models which operate on both text and tabular data, in this case applied to the mimic-iii clinical dataset (mimic-iii) @xcite . we evaluate two fairness algorithms: equalized-odds through post-processing, which is agnostic to the underlying classifier, and word embedding debiasing which is a text-specific technique. we show that ensembling classifiers trained on structured and unstructured data, along with the aforementioned fairness algorithms, can both improve performance and mitigate unfairness relative to their constituent components. we also achieve strong results on several mimic-iii clinical benchmark prediction tasks using a dual modality ensemble; these results may be of broader interest in clinical machine learning @xcite . ## results and analysis ihm phenotyping auc prc auc roc macro aucroc overall aucroc harutyunyan et. al (2019) -no text 0.515 0.862 0.776 0.825 khadanga et. al (2019) -ensemble 0.525 0.865 --ours -text only 0.472 0.815 0.766 0.829 ours -text only + biowordvec 0.489 0.841 0.771 0.837 ours -ensemble 0.582 0.880 0.822 0.861 ours -ensemble + biowordvec 0.582 0.886 0.829 0.870 3 ). this holds even when fairness/debiasing techniques are applied, emphasizing the overall effectiveness of leveraging multi-modal data. however, the ensemble's improvements in performance do not directly translate to improvements in fairness; see the true positive (tp) graph in figure 3 , where the maximum tp gap remains consistent under the ensemble. ## discussion in this paper, we propose a novel multimodal fairness task for the mimic-iii dataset, based on equal-ized odds. we provide two baselines: a classifieragnostic fairness algorithm (equalized odds postprocessing) and a text-specific fairness algorithm (debiased word embeddings). we observe that both methods generally follow the fairness performance tradeoff seen in single-modality tasks. eo is more effective at reducing the disparities in group-specific error rates while word-embedding debiasing has better performance. future work can consider more generalized notions of fairness such as preferences-based frameworks, or extend text-specific fairness to contextualized word embeddings @xcite . further analysis of the fairness performance tradeoff, especially in multimodal settings, will facilitate equitable decision making in the clinical domain. ## acknowledgements we would like to acknowledge vector institute for office and compute resources. we would also like to thank matt gardner for his help with answering questions when using allennlp @xcite . john chen and safwan hossain are funded by an ontario graduate scholarship and a vector institute research grant. ian berlot-attwell is funded by a canada graduate scholarships-master's, and a vector institute research grant. frank rudzicz is supported by a cifar chair in ai.