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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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How AI can learn from the law: putting humans in the loop only on appeal
I Glenn Cohen1,2, Boris Babic3, Sara Gerke4,5
1The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School, The Project on Precision Medicine, Artificial Intelligence, and the Law (PMAIL), Cambridge, MA, USA. igcohen@law.harvard.edu.
NPJ Digital Medicine
|August 25, 2023
Summary
Integrating human expertise into artificial intelligence (AI) and machine learning (ML) decisions is crucial. An appeals process allows human experts to review AI/ML judgments, enhancing accuracy and efficiency.
Area of Science:
- Computer Science
- Medical Ethics
- Public Policy
Background:
- Growing use of AI/ML in decision-making.
- Public reluctance to fully automate expert judgment.
- Need for effective human-AI/ML collaboration.
Purpose of the Study:
- Propose a model for integrating human expertise with AI/ML judgments.
- Address the conflict between algorithmic decisions and human oversight.
- Enhance the efficiency and accuracy of AI/ML systems.
Main Methods:
- Analogy with judicial appellate processes.
- Focus on human expert review of initial AI/ML decisions.
- Application to medical AI/ML use cases.
Main Results:
- Human-in-the-loop via appeals is an efficient labor division.
- Humans provide nuanced reasoning and case-specific insights.
- AI/ML error correction is achieved while maintaining efficiency.
Conclusions:
- Appeals-based human review effectively combines AI/ML efficiency with human expertise.
- This model is applicable across various domains, especially medicine.
- Balances automated decision-making with essential human oversight.
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