AI Life Cycle Core Principles Legislative Scoring Analysis H.R. 206 – Healthy Technology Act of 2023
Unmodified Report
Below is an unmodified legislative scoring report using the AI Life Cycle Core Principles framework. It used Claude Opus 4.1.
To learn more about the AI Life Cycle Core Principles framework, check out this post.
This isn’t perfect, but it’s already good and will get better as I continue to fine-tune the prompt.
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Executive Summary
This report analyzes H.R. 206, the “Healthy Technology Act of 2023,” against the AI Life Cycle Core Principles using a structured scoring methodology. The Act amends the Federal Food, Drug, and Cosmetic Act to allow artificial intelligence and machine learning technologies to prescribe drugs under specific regulatory conditions. The analysis identifies that while the Act addresses basic regulatory oversight through state and FDA approval requirements, it lacks provisions for most core AI principles including transparency, consent, accountability, and bias mitigation. The scoring methodology incorporates standards from ISO/IEC, IEEE, and NIST frameworks to provide comprehensive guidance for AI governance.
Verified Section Index
Section 1: SHORT TITLE
Preview: “This Act may be cited as the ‘Healthy Technology Act of 2023′”
Section 2: PRESCRIPTION OF DRUGS BY ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING TECHNOLOGIES
Preview: “Section 503(b) of Federal Food, Drug, and Cosmetic Act (21 U.S.C. 353(b)) is amended by adding at the end the following:”
Section 2(A): AI/ML must be “authorized pursuant to a statute of the State involved to prescribe the drug involved”
Section 2(B): AI/ML must be “approved, cleared, or authorized under section 510(k), 513, 515, or 564”
Scoring Methodology
Evidence Components:
- Keyword Evidence (10% weight): Measures distinct keywords from principle definitions found in the bill text, with saturation at 5 keywords.
- Definition Alignment (20% weight): Binary assessment of whether operative sentences semantically match principle definitions.
- Obligations in Verified Sections (40% weight): Counts unique sections containing enforceable obligations using terms like “shall,” “must,” “required to,” “authorized,” or “approved.”
- Enforcement Strength (30% weight): Evaluates whether enforcement provisions apply to the principle, only credited if operative obligations exist.
Composite Score Calculation: Raw score = 5 × (weighted sum of evidence components). Scores are rounded using standard rounding (4.5→5, 4.4→4). If no operative provisions exist for a principle, the score is capped at 1.
Zero-Score Guardrails: Transparency and Consent principles receive automatic zero scores if the Act lacks written disclosure requirements for AI use or patient consent requirements, regardless of other evidence.
Standards Mapping: The methodology maps principles to published standards from ISO/IEC, IEEE, and NIST to provide implementation guidance and regulatory alignment.
Main Scoring Table
| Principle | Score | Brief Rationale | Sections | Maps to Standard |
|---|---|---|---|---|
| Permit | 4/5 | Act explicitly requires government permits: state authorization to prescribe drugs and FDA approval/clearance under specified regulatory sections. The dual-permit requirement aligns strongly with the principle definition of AI systems being “subject to and compliant with a government issued permit.” | Section 2(A), Section 2(B) | ISO-IEC-42006, ISO-IEC-TR-42106, ISO-IEC-42001:2023 |
| Governance | 2/5 | Act establishes dual governance through state statutory authorization and FDA regulatory oversight, but lacks detailed governance framework. Missing elements include: continuous oversight mechanisms, clear governance roles and responsibilities, and structured decision-making processes for AI system operations. | Section 2(A), Section 2(B) | ISO-IEC-42006, ISO-IEC-TR-42106, ISO-IEC-42001:2023, ISO-IEC-42005:2025, IEEE-7000-2021, IEEE-7002-2022, IEEE-7003-2024, ISO-31000:2018, NIST-AI-600-1, NIST-AI-RMF-1.0, NIST-PRIV-1.0, NIST-SP-1270, NIST-SP-800-53r5 |
| Safety | 2/5 | FDA approval/clearance process under sections 510(k), 513, 515, or 564 incorporates safety evaluation, though Act lacks explicit safety performance requirements. The principle calls for systems that “operate without causing harm” and “proactively prevent unsafe states,” which requires more than regulatory approval alone. | Section 2(B) | ISO-IEC-42105, ISO-IEC-25059, ISO-IEC-42005:2025, ISO-IEC-23894:2023 |
Potential Gaps and Future Legislative Opportunities
| Principle | Recommendation | Maps to Standard |
|---|---|---|
| Transparency | Require AI/ML prescribing systems to provide open, accessible, and understandable disclosure of their data practices, logic, and decision-making processes to patients, healthcare providers, and regulators, ensuring every significant aspect of the system’s operation is comprehensible to relevant stakeholders | ISO-IEC-TR-42106, IEEE-7001-2021, ISO-IEC-25059, ISO-IEC-42005:2025, IEEE-7000-2021, NIST-AI-RMF-1.0 |
| Consent | Mandate that AI/ML systems obtain legally valid consent from patients before prescribing medications, ensuring the system design continuously maintains alignment with end user consent throughout the prescribing process | ISO-IEC-27090, IEEE-7002-2022 |
| Accountability | Establish clear accountability mechanisms requiring AI/ML system developers and deployers to take responsibility for system outputs, with defined liability frameworks for AI-prescribed medications and adverse outcomes | ISO-IEC-42006, ISO-IEC-TR-42106, ISO-IEC-42001:2023, IEEE-7001-2021, IEEE-7002-2022, ISO-31000:2018, NIST-AI-600-1, NIST-AI-RMF-1.0, NIST-PRIV-1.0 |
| Accuracy | Require AI/ML prescribing systems to demonstrate and maintain defined accuracy thresholds, with regular validation against clinical standards and continuous monitoring of prescription appropriateness | ISO-IEC-TS-29119-11, ISO-IEC-TR-42106, ISO-IEC-22989:2022 |
| Bias | Mandate bias assessment and mitigation strategies in AI/ML prescribing systems to prevent discriminatory prescribing patterns based on patient demographics, socioeconomic status, or other protected characteristics | ISO-IEC-22989:Amd1, IEEE-7003-2024, ISO-IEC-42005:2025, ISO-IEC-24027:2021, NIST-SP-1270 |
| Privacy | Ensure AI/ML systems respect and protect patient personal data in alignment with Fair Information Principles, processing health information only as necessary for prescribing decisions with appropriate safeguards | ISO-IEC-27090, ISO-IEC-42001:2023, IEEE-7002-2022, NIST-AI-600-1, NIST-AI-RMF-1.0, NIST-PRIV-1.0, NIST-SP-800-53r5 |
| Explainability (XAI) | Require AI/ML systems to provide clear explanations of prescribing decisions that healthcare providers and patients can understand, enabling informed consent and clinical oversight | ISO-IEC-TR-42106, IEEE-7001-2021, ISO-IEC-42005:2025, ISO-IEC-22989:2022 |
| Fairness | Ensure AI/ML prescribing systems provide equitable treatment across all patient populations, preventing disparate impacts and ensuring equal access to appropriate medications | ISO-IEC-22989:Amd1, IEEE-7003-2024, ISO-IEC-42005:2025, ISO-IEC-22989:2022, NIST-SP-1270 |
| Security | Mandate robust security measures to protect AI/ML prescribing systems from unauthorized access, manipulation, or adversarial attacks that could compromise patient safety | ISO-IEC-27090, ISO-IEC-27001:2022, NIST-AI-600-1, NIST-PRIV-1.0, NIST-SP-800-53r5 |
| Human-Centered | Require AI/ML systems to prioritize human welfare, maintaining meaningful human oversight of prescribing decisions and ensuring technology augments rather than replaces clinical judgment | ISO-IEC-42105, IEEE-P7008, IEEE-7010-2020, ISO-IEC-42005:2025, IEEE-7000-2021 |
| Reliability | Establish reliability standards requiring AI/ML systems to perform consistently and predictably across diverse clinical scenarios and patient populations | ISO-IEC-TS-29119-11, ISO-IEC-TR-42106, ISO-IEC-20547-3:2020 |
| Data Stewardship | Implement comprehensive data governance requirements for training data, ensuring quality, representativeness, and appropriate use of patient information in AI/ML model development | ISO-IEC-24970, ISO-IEC-25059, ISO-IEC-TR-42103, IEEE-7002-2022, ISO-IEC-20546:2019, NIST-PRIV-1.0, NIST-SP-1270 |
| Ethics | Require adherence to medical ethics principles in AI/ML prescribing, ensuring beneficence, non-maleficence, autonomy, and justice in automated prescribing decisions | IEEE-P7008, IEEE-7010-2020, ISO-IEC-42001:2023, ISO-IEC-42005:2025, IEEE-7000-2021, IEEE-7003-2024 |
| Robust | Mandate robustness testing to ensure AI/ML systems maintain performance under varied conditions, edge cases, and potential adversarial inputs | ISO-IEC-TS-29119-11, ISO-IEC-TR-42106, ISO-IEC-25059 |
| Metrics | Establish standardized metrics for evaluating AI/ML prescribing system performance, including clinical outcomes, safety indicators, and equity measures | ISO-IEC-42006, ISO-IEC-TR-42106, ISO-IEC-42001:2023, ISO-31000:2018, NIST-AI-600-1, NIST-AI-RMF-1.0 |
| Equity | Ensure AI/ML prescribing systems actively promote equitable healthcare access and outcomes, addressing disparities and ensuring underserved populations receive appropriate care | IEEE-7003-2024, IEEE-7010-2020, ISO-IEC-24027:2021, NIST-SP-1270 |
| Accessibility | Design AI/ML systems with user-friendly interfaces and optimal user experience methods that facilitate end user understanding of algorithms and outcomes, ensuring systems are accessible to all users including those with disabilities | ISO-IEC-TR-42106, IEEE-7001-2021, ISO-IEC-42005:2025 |
| Interpretability | Ensure AI/ML system outputs are understandable by humans, particularly by experts, auditors, and users responsible for assessing and documenting compliance | ISO-IEC-TR-42106, IEEE-7001-2021 |
| Fidelity | Require AI/ML systems to maintain faithfulness to their intended purpose and clinical guidelines, ensuring outputs accurately reflect the underlying medical knowledge and best practices | ISO-IEC-20546:2019, ISO-IEC-23053:2022 |
| Resilience | Build AI/ML systems with the ability to maintain operations and recover quickly from disruptions, ensuring continuous availability of critical prescribing capabilities | ISO-IEC-TR-42103 |
Standards Framework Analysis
The analysis reveals significant alignment opportunities with international and national standards frameworks:
NIST Standards Coverage
- NIST AI Risk Management Framework 1.0 (NIST-AI-RMF-1.0): Provides comprehensive guidance for governance, accountability, metrics, privacy, and transparency – critical gaps in the current Act
- NIST-AI-600-1: Addresses governance, accountability, metrics, security, and privacy considerations for AI systems
- NIST Privacy Framework (NIST-PRIV-1.0): Offers detailed privacy, governance, and data stewardship requirements essential for healthcare AI
- NIST-SP-1270: Provides specific guidance on bias mitigation, fairness, and equity in AI systems
- NIST-SP-800-53r5: Security and privacy controls applicable to AI/ML prescribing systems
ISO/IEC Standards Coverage
- ISO-IEC-42001:2023 – AI management system requirements
- ISO-IEC-42005:2025 – AI system impact assessment
- ISO-IEC-42006 – Requirements for bodies providing audit and certification
- ISO-IEC-TR-42106 – Overview of AI system characteristics and related concepts
IEEE Standards Coverage
- IEEE-7000-2021 – Model process for addressing ethical concerns
- IEEE-7001-2021 – Transparency of autonomous systems
- IEEE-7002-2022 – Data privacy process
- IEEE-7003-2024 – Algorithmic bias considerations
- IEEE-7010-2020 – Wellbeing metrics for ethical AI
Key Findings
Strengths:
- The Act establishes basic regulatory oversight through dual authorization requirements (state and FDA)
- Strong alignment with the Permit principle through explicit government authorization requirements
- Foundation for governance through regulatory oversight mechanisms
Critical Gaps:
- Patient Protection: No requirements for transparency, consent, or explainability of AI prescribing decisions
- Equity and Fairness: No provisions to prevent discriminatory prescribing or ensure equitable access
- Security and Privacy: Lacks specific data protection and system security requirements despite NIST framework availability
- Accountability: No liability framework or responsibility assignment for AI-prescribed medication outcomes
- Performance Standards: Missing accuracy, reliability, and robustness requirements for clinical deployment
Conclusion
The Healthy Technology Act of 2023 takes an important first step in establishing regulatory oversight for AI/ML prescribing systems through state authorization and FDA approval requirements. However, the Act addresses only 3 of 37 core AI principles, leaving significant gaps in transparency, accountability, bias mitigation, and patient protection. The availability of comprehensive standards from NIST (including the AI Risk Management Framework), ISO/IEC, and IEEE provides a clear roadmap for strengthening the legislation. Future legislative efforts should incorporate the recommendations outlined above, leveraging these established standards frameworks to ensure AI/ML prescribing systems operate safely, fairly, and transparently while maintaining patient trust and clinical effectiveness.
Report generated using AI Life Cycle Core Principles Scoring Methodology v7