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Algorithmic bias, generalist models, and clinical medicine

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Abstract

The technical landscape of clinical machine learning is shifting in ways that destabilize pervasive assumptions about the nature and causes of algorithmic bias. On one hand, the dominant paradigm in clinical machine learning is narrow in the sense that models are trained on biomedical data sets for particular clinical tasks, such as diagnosis and treatment recommendation. On the other hand, the emerging paradigm is generalist in the sense that general-purpose language models such as Google’s BERT and PaLM are increasingly being adapted for clinical use cases via prompting or fine-tuning on biomedical data sets. Many of these next-generation models provide substantial performance gains over prior clinical models, but at the same time introduce novel kinds of algorithmic bias and complicate the explanatory relationship between algorithmic biases and biases in training data. This paper articulates how and in what respects biases in generalist models differ from biases in prior clinical models, and draws out practical recommendations for algorithmic bias mitigation. The basic methodological approach is that of philosophical ethics in that the focus is on conceptual clarification of the different kinds of biases presented by generalist clinical models and their bioethical significance.

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Notes

  1. For an introduction to machine learning in medicine see Rajkomar et al. [61]. For further reading see Shamout et al. [65], Secinaro et al. [64], Lee and Lee [43].

  2. Performance biases can also arise, among other reasons, from misrepresentative training data such as data sets that employ proxy variables or data labels that systematically distort the circumstances of a disadvantaged group [38, 52]. See Section 2.3 for discussion.

  3. For an excellent discussion on the nature and causes of algorithmic bias simpliciter, that is, outside the specific context of clinical medicine, see [16].

  4. The problem is underscored by the fact that absent equal base rates across subpopulations or perfect predictive performance binary classifiers cannot equalize precision and false positive/negative rates [7]. See Kleinberg et al. [41] for an analogous result for continuous risk scores. For discussion of the significance of the fairness impossibility theorems for healthcare see Grote and Keeling [28] and Grote and Keeling [29].

  5. The implication here is not that all performance biases are explained by biases in training data, as biases can arise at every stage of the machine learning pipeline [61]. Rather, the claim is that performance biases (at least in healthcare, where demographic data biases are widespread and pervasive) in a broad class of cases arise due to biases in data sets, such as under-representative or misrepresentative training data. Such is the extent of data biases that it makes sense for organizations like the to orient their general bias mitigation advice around data representativeness [c.f. 17].

  6. This formulation is rough, because strictly speaking tokens and not words are inputted into the function, i.e., input sequences of text are first tokenized [see 26].

  7. A third case bracketed here is sequence-to-sequence models such as Google’s T5 that include an encoder and a decoder [58].

  8. These examples are illustrative and are not intended as an exhaustive taxonomy.

  9. Sun et al. [71, p.206] note that ‘the use of negative descriptors might not necessarily reflect bias among individual providers; rather, it may reflect a broader systemic acceptability of using negative patient descriptors as a surrogate for identifying structural barriers.

  10. Note that this issue is not unique to LLMs. Similar considerations hold for models and research studies that rely on discrete EHR data, which can also encode biases [c.f. 63].

  11. Ethical fine-tuning can also be achieved via non-supervised approaches such as Reinforcement Learning from Human Feedback [2, 53]. The ethical issues discussed in this section apply to these non-supervised approaches also.

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Acknowledgement

The author is grateful to Michael Howell, Heather Cole-Lewis, Lisa Lehmann, Diane Korngiebel, Thomas Douglas, Bakul Patel, Kate Weber, and Rachel Gruner for helpful comments, alongside participants at the Workshop on the Ethics of Influence at the Uehiro Centre for Practical Ethics at the University of Oxford.

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This study was funded by Google LLC and/or a subsidiary thereof (‘Google’).

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Keeling, G. Algorithmic bias, generalist models, and clinical medicine. AI Ethics (2023). https://doi.org/10.1007/s43681-023-00329-x

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