Commentary about the role of AI in bioethics for the 10th anniversary issue of AJOB: Empirical Bioethic

Conitzer, Vincent

Sinnott-Armstrong, Walter

Skorburg, Joshua August

English

PhilPapers

Full Terms & Conditions of access and use can be found athttps://www.tandfonline.com/action/journalInformation?journalCode=uabr21AJOB Empirical BioethicsISSN: 2329-4515 (Print) 2329-4523 (Online) Journal homepage: https://www.tandfonline.com/loi/uabr21AI Methods in BioethicsJoshua August Skorburg, Walter Sinnott-Armstrong & Vincent ConitzerTo cite this article: Joshua August Skorburg, Walter Sinnott-Armstrong & VincentConitzer (2020) AI Methods in Bioethics, AJOB Empirical Bioethics, 11:1, 37-39, DOI:10.1080/23294515.2019.1706206To link to this article:  https://doi.org/10.1080/23294515.2019.1706206Published online: 25 Feb 2020.Submit your article to this journal View related articles View Crossmark dataCOMMENTARYAI Methods in BioethicsJoshua August Skorburg, Walter Sinnott-Armstrong, and Vincent ConitzerDuke University, Durham, North Carolina, USASeemingly every week, new artificial intelligence (AI)applications are being developed to assist and auto-mate various forms of medical decision-making.Examples abound, but some notable instances includerobot-assisted surgery, precision medicine, drug dis-covery and drug interactions, assessment of suiciderisk from electronic health records or social mediaposts, or automated diagnosis on the basis of geneticsequencing, images, speech, text, and even mouse cur-sor movements.In parallel, a new wave of scholarship in bioethicsis exploring the ethical, legal, and social implicationsof these AI applications in medicine. This workfocuses, among other areas, on how to protect patientprivacy in the face of the large-scale data collectionrequired to train AI systems; how AI applications canreproduce and exacerbate existing biases, and also cre-ate new forms of inequality; and how increasing reli-ance on AI technologies may render healthcare lesspatient-centered. Again, examples abound, but somehigh-profile work in this vein has revealed racialbiases in neural networks trained to identify skin can-cer (Adamson and Smith 2018). Related researchraises ethical concerns about how new tools, such asautomated speech-based diagnosis of psychopathology,may perform poorly on non-native English speakers.Similar work considers the potentially diminished roleof patients’ subjective experiences, in light of prolificdata-mining of health records (Ruckenstein andSch€ull 2017).Crucial as these concerns are, far less attention hasbeen paid to the role that AI applications might playin bioethics itself. Might AI improve bioethicalinquiry? Could AI bolster methods in empirical bio-ethics? In this commentary, we will argue that theanswers to these questions are a cautiously optimistic“yes” and that empirical bioethicists’ engagement withAI need not be limited to ethical, legal, and socialimplications.To see this, consider kidney transplants. There areregrettably not enough donors to supply kidneys to allof the patients in need. This raises a moral problem.Who, among many needy patients, should receive akidney when one becomes available? These decisionsare often based on features such as compatibility, age,and time on the waiting list. But there are longstand-ing debates in bioethics about whether other featuresshould be considered, and which ethical principlesought to guide decisions about kidney allocation(Childress 1989).To the extent that kidney allocation is increasinglydetermined by algorithms, these questions willdemand answers in the form of design choices aboutwhat such algorithms are optimizing for, which fea-tures are included or excluded, and how they areweighted. These ethical issues regarding design choicesare right in the empirical bioethicist’s wheelhouse.Indeed, we have developed a method (Freedmanet al. 2018) for the case of kidney transplants that wethink can also generalize to other issues in bioethics.In a forthcoming paper, we call this method ArtificialImproved Democracy (AID) (Sinnott-Armstrong andSkorburg forthcoming).The first step in this method is to ask experts and lay-people which features they think ought and ought notfigure into decisions about kidney allocations. Then, afterediting responses for clarity, redundancy, etc., we can usethis curated list of features to construct forced-choicescenarios in which one (or a set of) valued feature(s) con-flicts with another (or a set of) valued feature(s).For example, our results suggest that most laypeo-ple do think that age and time on the waiting listshould be considered. Most people also think thatrace and religion should not be considered. But fea-tures such as mental health and criminal records aremore controversial (Doyle et al. in prep).In a controlled experimental setting, we can then ask,“who should get the kidney?” when Patient A is 34 yearsCONTACT Joshua August Skorburg g.skorburg@gmail.com Duke University, Durham, 27708-0187 USA. 2020 Taylor & Francis Group, LLCAJOB EMPIRICAL BIOETHICS2020, VOL. 11, NO. 1, 37–39https://doi.org/10.1080/23294515.2019.1706206old, with two dependents, and a history of consumingthree alcoholic beverages a day and Patient B is 46 yearsold with one dependent and no history of alcohol con-sumption. After running hundreds and thousands ofthese choice trials, we can then use machine learningtechniques to reveal which of the conflicting featuresreally do seem to drive people’s decisions about kidneyallocations, as well as how different features interactwith one another to produce this decision. On the basisof these findings, the third step in our method is tobuild models to predict individual-level and group-leveldecisions. In turn, these predictive models can be itera-tively improved by being applied to new scenarios.This method has several advantages. First, we cancompare (in experts and laypeople alike) which fea-tures people think should be considered in allocatingkidneys. Such information could reveal important dif-ferences between, for example, the values of doctorsor hospital administrators on the one hand, and thevalues of patients or community stakeholders, on theother. To the extent that participatory research andpatient-centered care (Department of Health 2009) areguiding ideals, the data collected in our studies can beused to inform policies to better align healthcare serv-ices with the values of the patients and communitiesutilizing them.Second, we can compare which features people sayshould guide allocation decisions with which featuresactually seem to guide their (hypothetical) allocationdecisions. When there is a gap between them, we canupdate the model to more closely approximate con-sensus values. For example, if people consistently saythat race or religion should not be considered whendeciding who gets a kidney, yet in their allocationdecisions these features do seem to play a role (asreflected in our models of them), then we can updatethe models to no longer take these features intoaccount in order to reflect the consensus.Third, our method helps to shed light on importantmechanisms of medical decision-making, yielding bothdescriptive and explanatory accounts. The more accur-ate such models become, the more we can understandthe processes driving decisions about kidney alloca-tions. And the more we understand about these proc-esses, the better we will be able to guide design choicesto build systems that instantiate both actual human val-ues and more ideal decision procedures.For these reasons, we think there is a strong case forusing AI methods in empirical bioethics. The exampleof kidney allocation is meant to serve as proof of con-cept. We think the components of this method (first,design surveys to discern morally relevant features;second, construct forced-choice conflict scenarios withthese features; third, iterate predictive models on thebasis of choice data) can generalize to other issues inempirical bioethics. This is clear enough for closelyrelated cases, such as liver, lung, or heart transplant pro-grams. But the method could also be used in casesinvolving other limited medical resources, such asscarce hospital beds or clinicians’ time in critical caresettings. We can imagine other applications in decisionsinvolving life-sustaining treatments, experimentaltherapies, emergency medicine, or end-of-life issues.To be sure, there are limitations, challenges, anddifficulties which we have not addressed in this shortcommentary. But hopefully the foregoing has dem-onstrated that empirical bioethicists’ engagementwith AI technologies need not be limited to ethical,legal, and social implications which have tended todominate the recent literature. Indeed, if ourresearch program proves fruitful, then these emerg-ing AI tools will open new modes of empirical andnormative inquiry—tasks for empirical bioethicists ifthere ever were any.Author contributionsJAS, WSA, and VC all contributed to the conception,drafting, and editing of the manuscript.Conflicts of interestNone.Ethical approvalThe studies referenced in this manuscript were approvedby the institutional review board at Duke University.ReferencesAdamson, A. S., and A. Smith. 2018. Machine learningand health care disparities in dermatology. JAMADermatology 154 (11):1247–8. doi: 10.1001/jamadermatol.2018.2348.Childress, J. F. 1989. Ethical criteria for procuring and dis-tributing organs for transplantation. Journal of HealthPolitics, Policy and Law 14 (1):87–113. doi: 10.1215/03616878-14-1-87.Department of Health. 2009. Putting people at the heart ofcare. Retrieved from https://webarchive.nationalarchives.gov.uk/20130123200554/http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAndGuidance/DH_106038Doyle, et al. (in prep). Which features of patients shoulddetermine who gets a kidney?38 J. A. SKORBURG ET AL.Freedman, R., J. S. Schaich Borg, W. Sinnott-Armstrong,J. P. Dickerson, and V. Conitzer. 2018. April. Adapting akidney exchange algorithm to align with human values.In Thirty-Second AAAI Conference on ArtificialIntelligence. doi: 10.1145/3278721.3278727.Ruckenstein, M., and N. D. Sch€ull. 2017. The datafication ofhealth. Annual Review of Anthropology 46 (1):261–78.doi: 10.1146/annurev-anthro-102116-041244.Sinnott-Armstrong, W., and J.A. Skorburg. (forthcoming).How AI can AID bioethics. Journal of Practical Ethics.AJOB EMPIRICAL BIOETHICS 39

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