The Human Values in AI Healthcare

    The Human Values in AI Healthcare

    Z
    @ZakTheK
    4 Followers
    5 months ago 434

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    Key Insights

    The Human Values Project
For The AI We Want
Isaac S. Kohane, MD, PhD
    1/20
    What values do we expect from our doctor?
    2/20
    Patient management platform,
promoting personalized, 
preventative & proactive 
medical care
CLALIT
    3/20
    The Human Values in AI Healthcare - Page 4
    4/20
    Patient prioritized for 
proactive preventive 
intervention
    5/20
    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)
    6/20
    The Human Values in AI Healthcare - Page 7
    7/20
    Values and 
stakeholders
    8/20
    Big stakes. Present challenge
    9/20
    Classic Ethical
Framing
Principles
    10/20
    The Human Values in AI Healthcare - Page 11
    11/20
    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.
    12/20
    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.
    13/20
    Case Study
• Concordance
    14/20
    Case Study
• Consistency
• How do you feel if 
your doctor 
changes her 
decisions a lot?
    15/20
    Case Study
• Alignability
    16/20
    Steerability wrt Decision Vector D
    17/20
    Alignment Compliance Index
    18/20
    The Human Values in AI Healthcare - Page 19
    19/20
    The Human Values in AI Healthcare - Page 20
    20/20

    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
    • 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)
    • 8. Values and stakeholders
    • 9. Big stakes. Present challenge
    • 10. Classic Ethical Framing Principles
    • 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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