The Human Values in AI Healthcare

The Human Values in AI Healthcare

• 1. The Human Values Project For The AI We Want Isaac S. Kohane, MD, PhD

• 2. What values do we expect from our doctor?

• 3. Patient management platform, promoting personalized, preventative & proactive medical care CLALIT

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• 5. Patient prioritized for proactive preventive intervention

• 6. Patient prioritization for proactive care These factors all influence the utility of prioritizing a specific patient, resembling the motivation behind the QALY framework The challenge: How should these factors be combined into a single prioritization schema? We currently identify 3 patient-level components that should potentially take place in the prioritization process: 1. The patient’s risk for the outcome we aim to prevent (can be expressed as an absolute, individualized predicted risk) 2. The patient’s life expectancy (can be evaluated using a relevant prediction model or with age as a proxy) 3. The significance and quantity of care gaps that the proactive intervention can address (can be quantified according to the list of practical care recommendations)

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• 8. Values and stakeholders

• 9. Big stakes. Present challenge

• 10. Classic Ethical Framing Principles

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• 12. Why do we need both normative model & personal model • Preferences of individuals may not align with overarching policies. • Preferences across stakeholders (e.g. doctors, patients, public health) may not be resolvable with a consistent set of decisions. • Knowledge of preferences of classes of individuals allows automated personalization. For example: • Parents of children with autism with severe developmental delay. • Individuals undiagnosed and and rapidly weakening. • Young adults concerned about their family history of heart disease. • Elderly patients with painful terminal disease. • Knowledge of preferences of classes of individuals will flag lack of alignment with explicit institutional policy.

• 13. What do we know about medical preferences in LLMs. • Precious little (data for pre-trained model, data for RLHF++, in-context steering). • We do not know: Which models ‘out of the box’ are best aligned • We do not know: How consistent are they in following a particular perspective. • We do not know. How well they can be moved to a specific set of preferences (aka aligned) • We do not know: Can they represent perspectives of all parties. • We do not know: Where in the multiverse of medical decisions their decisions most resemble normative or particular patient context.

• 14. Case Study • Concordance

• 15. Case Study • Consistency • How do you feel if your doctor changes her decisions a lot?

• 16. Case Study • Alignability

• 17. Steerability wrt Decision Vector D

• 18. Alignment Compliance Index

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