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Against explainability requirements for ethical artificial intelligence in health care

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Abstract

It is widely accepted that explainability is a requirement for the ethical use of artificial intelligence (AI) in health care. I challenge this Explainability Imperative (EI) by considering the following question: does the use of epistemically opaque medical AI systems violate existing legal standards for informed consent? If yes, and if the failure to meet such standards can be attributed to epistemic opacity, then explainability is a requirement for AI in healthcare. If not, then based on at least one metric of ethical medical practice (informed consent), explainability is not required for the ethical use of AI in healthcare. First, I show that the use of epistemically opaque AI applications is compatible with meeting accepted legal criteria for informed consent. Second, I argue that human experts are also black boxes with respect to the criteria by which they arrive at a diagnosis. Human experts can nonetheless meet established requirements for informed consent. I conclude that the use of black-box AI systems does not violate patients’ rights to informed consent, and thus, with respect to informed consent, explainability is not required for medical AI.

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Notes

  1. Deep learning is a specific type of artificial intelligence which refers to complex forms of machine learning, like neural networks with several layers. Epistemic opacity and explainability imperatives largely concern deep learning systems.

  2. Throughout this manuscript, when I refer to AI systems, I mean deep learning systems specifically.

  3. I accept that informed consent preserves and upholds certain bioethical values like personal autonomy and non-domination. I also assume that existing legal requirements and guidelines adequately secure informed consent. I set aside broader questions and legitimate concerns about the fundamental ethical value of informed consent or the means by which it is granted.

  4. I will primarily use the terms explainability and transparency, though the literature vacillates between a family of terms including: transparency, interpretability, surveyability, explicability, etc. There have been serious efforts to distinguish between these different terms [19] and to highlight the importance of not conflating these terms (Herzog 2022). For my purposes, they will function in similar ways—to either mitigate or eliminate epistemic opacity in AI applications. As such, I will follow scholars, like Ursin et al. [31], who include these terms under the umbrella concept of “explicability” or “explainability” and focus on “explainability”. The finer distinctions are valuable but beyond the scope of my more general argument that epistemic opacity does not violate patients’ right to informed consent.

  5. See for example: London [18], Zerilli et al. (2019), and Duran and Jongsma (2021).

  6. Some exceptions exist, including diagnostic screening for STDs (such as HIV) and genetic tests which, in many jurisdictions, do require the patients’ consent.

  7. Such records are examples of peer-to-peer explanations of the sort that Holzinger et al. [11] seek to define for AI in the medical domain.

  8. Drusen are yellow deposits under the retina that are made up of lipids and proteins.

  9. Ground truth was based on the determination of retinal specialists with over 5-years experience in diabetic retinopathy grading. The DLS was shown to outperform two trained senior professional graders (non-retinal specialists) with over five years experience by reference to the grading of the retinal specialists. For example, two trained graders and the DLS were given a retinal image to grade. The DLS outperformed the two trained graders because it gave the correct grading more often than the trained graders, where the correct grading was determined by the retinal specialists’ grade.

  10. This is in line with Muller’s (2021) construal of the explainability required by AI regulations (like GDPR) as reflecting demands for justification first and foremost.

  11. I thank Eric Winsberg for bringing this point to my attention.

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  1. Department of Philosophy, Indiana University, Bloomington, IN, USA

    Suzanne Kawamleh

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Kawamleh, S. Against explainability requirements for ethical artificial intelligence in health care. AI Ethics 3, 901–916 (2023). https://doi.org/10.1007/s43681-022-00212-1

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