Example Conversation With GPT On AI Development And Usage Policy, Demonstrating Collaborative Context Practices

What follows is a real conversation I had with GPT (specifically ChatGPT-o1) regarding the implementation of AI development and usage standards at a fictitious company. I chose this subject as it’s one that’s real and relevant for many of us, and also a topic that most of us don’t yet know how to navigate. This isn’t SOC2 or GDPR. The rules haven’t been written yet. Yet you still need to stay ahead. The final challenge is that while the dust hasn’t settled, there’s a lot of dust. All of which makes this a perfect place to show the power of effectively collaborating with AI, enabling to help, not just search.

Copy of conversation with ChatGPT-o1 (you can find the same conversation within ChatGPT here.):

ME:

I’m the CTO for a mid-size public company in the healthtech/healthcare AI space, where my company develops automation and AI tools/software for the healthcare industry (e.g., AI co-pilots, automated documentation, AI imagery reviews). It’s clear that regulation of the development of such products and services is coming, but we don’t yet know what they’ll be or how/if they’ll apply to us. This moment reminds me of when GDPR came about, and the lengths companies went to to become compliant before they were impacted. I want to ensure my company is aware of whatever it needs to be in regards to AI regulation, and that we are proactively prepared for any impacts to us. Please assume the role of the world’s best AI strategist, specializing in policy regarding the responsible development of AI. I’d like your help assessing the regulatory landscape, compiling the developments & policies to watch, and crafting a strategy to keep my company ahead of the curve. Before we begin, please ask (up to) the 10 most important questions you need answered in order to solidify your understanding and optimize your response/help.

ChatGPT:

Below 10 questions that will help me tailor guidance to your company’s specifics:

1. Current Regulatory Foundations: Which healthcare/tech regulatory frameworks do you already comply with (e.g., HIPAA in the US, FDA guidelines for medical devices, etc.)?
2. Scope of AI Solutions: What types of AI-driven products or features do you develop (e.g., clinical decision support, AI-assisted diagnostics, administrative automation, patient data analytics), and what is their level of clinical or operational impact?
3. Geographic Footprint: In which regions or countries do you operate or plan to operate, and how do local data-privacy or AI regulations (e.g., GDPR in the EU, emerging AI acts in various regions) currently affect your business?
4. Data Sources and Handling: What kinds of data do you train and test on (PHI, anonymized data, third-party data sets), and how do you ensure compliance with patient privacy, data security, and consent requirements?
5. Model Development & Lifecycle Management: How mature is your AI governance process (e.g., do you have a formal ML Ops framework, documented model risk-assessment processes, continuous monitoring)?
6. Risk Appetite & Corporate Culture: How proactive or conservative is your organization about regulatory risk? Are you willing to invest early for compliance, or do you prefer to wait for clearer rules?
7. Current Oversight and Auditing: Do you have any internal committees or external advisory boards overseeing ethical AI use, model bias testing, and regulatory compliance efforts?
8. Resources & Team Expertise: How large and specialized is your compliance or policy team, and do you have in-house legal/regulatory experts focused on AI specifically?
9. Partnerships & Collaboration: Do you partner with healthcare providers, research institutions, or other third parties that could influence or dictate certain compliance requirements?
10. Future Plans & Product Roadmap: What new AI-driven capabilities or expansions are in your pipeline, and do you anticipate them raising novel regulatory questions (e.g., direct patient-impacting diagnostics, insurance underwriting tools, etc.)?

Answering these questions will help me customize recommendations, highlight relevant regulatory trends, and plan a strategy that keeps your organization well ahead of potential AI compliance hurdles.

ME:

See answers below:

1. We currently comply with HIPAA and SOC2. But we’ve been hearing about new standards from IEEE, CHAI, the White House, which we don’t fully understand or know what applies.

2. Our current products include: AI-assisted diagnosis, AI documentation, and AI billing optimization.

3. We are currently operating in the US and Germany. We are compliant with GDPR, but we don’t know what AI standards may apply.

4. We process PHI data, including recorded conversations and demographic data. We comply with HIPAA and SOC2 to ensure proper handling of data and data security.

5. Our AI governance is nascent. We perform security reviews, penetration testing, and security audits. In addition, we periodically run a validation test set for the AI output.

6. We have a healthy risk appetite and want to be stage-appropriate for where we are. That being said, we do not want to risk patient data or data security.

7. No.

8. Our compliance team consists of 2 people who support this work as a part time, extra duty; they’re main focus is product development. We do not have a lot of depth or expertise in this area.

9. Yes, we do partner with medical institutions and healthcare providers, and part of the motivation to dig in here is coming from them asking more and wondering what’s needed to stray ahead of the curve in terms of AI compliance. No one wants to get caught off guard.

10. We plan on expanding into Therapy, which I could imagine may have it’s own implications.

ChatGPT:

Below is a strategic overview focusing on (1) key regulatory regimes and emerging guidelines to watch, (2) a recommended approach to aligning with them, and (3) concrete steps to keep your company ahead of potential AI regulations. The aim is to help you build a sustainable, proactive regulatory and ethical posture for your AI-driven healthcare products.

1. Key Regulatory and Standards Developments to Watch

United States

1. FDA and Software as a Medical Device (SaMD)

• The FDA is increasingly focused on regulating AI/ML-based medical devices, especially those supporting clinical decisions (e.g., AI-assisted diagnosis).
• Draft guidances have addressed “Good Machine Learning Practice” and “Predetermined Change Control Plans” for continuously learning software.

2. HHS/OCR on HIPAA & Health Data

• Ongoing guidance from the Department of Health and Human Services and the Office for Civil Rights on handling PHI in AI workflows (e.g., de-identification, encryption, secure cloud services).

3. White House Blueprint for an AI Bill of Rights

• This is not yet law but a policy framework that signals how the U.S. government may legislate AI fairness, transparency, and accountability in the future.
• Emphasizes data protections, algorithmic discrimination protections, and user consent.

4. NIST AI Risk Management Framework

• A voluntary framework developed by NIST to guide AI system development and risk management.
• Likely to be used as a reference for “best practices” in AI systems, especially for government contracting or future U.S. regulations.

European Union (Germany)

1. EU AI Act (Proposed)

• This forthcoming regulation will classify AI systems by risk level (e.g., unacceptable, high, limited, minimal). Healthcare is expected to be categorized as “high-risk,” meaning stringent requirements around data governance, transparency, post-market monitoring, etc.
• Although not final, it will likely require CE-mark style conformity assessments for AI systems that significantly impact patient care.

2. MDR/IVDR (Medical Device Regulation/In Vitro Diagnostic Regulation)

• If any of your AI-based tools fall under medical device classification, they may need compliance with EU MDR. AI that diagnoses or directly influences treatment decisions is often regulated as a medical device.

3. GDPR

• You already comply with GDPR, but note that the EU AI Act will layer on top, with explicit requirements for record-keeping, transparency, and “human oversight” for high-risk AI systems.

Industry Standards & Bodies

1. IEEE & ISO AI Standards

• IEEE has ongoing initiatives (e.g., IEEE P7000 series) around ethically aligned design, data privacy processes, algorithmic transparency, and bias mitigation.
• ISO/IEC (e.g., ISO/IEC 38507 for AI governance) is creating frameworks for governance of AI that align with risk-based approaches.

2. Center for Humane AI (CHAI), other Think Tanks

• Provide guidance on ethical AI design, fairness, bias testing, transparency, and governance that may be adopted or referenced by regulators and industry consortia.

2. Recommended Approach and Alignment

A. Map Your Existing & Future Products to Regulatory Categories

• AI-assisted diagnosis: Likely regulated as a “high-risk” or medical device under FDA (SaMD) and EU’s MDR/AI Act.
• AI documentation & billing: Less direct clinical impact, but still subject to HIPAA, potential future U.S. AI fairness laws (billing/coding fairness?), and GDPR data handling rules.
• Expanding into Therapy: Could trigger more stringent regulations if the AI is used to inform therapy plans or make mental/behavioral health recommendations.

B. Formalize AI Governance & Oversight

1. Establish an AI Oversight Committee

• Cross-functional group (Legal/Compliance, Product, Data Science, Clinical/Medical, Security).
• Reviews models and ensures alignment with existing HIPAA, GDPR, NIST, and anticipated EU AI Act frameworks.

2. Adopt a Risk Management Framework

• Consider leveraging NIST’s AI RMF to systematically identify and address potential risks (bias, security, privacy, safety).
• Build or integrate risk assessment tools specifically for AI that track model development lifecycle, updates, and known hazards.

3. Develop Ethical/Responsible AI Guidelines

• Leverage recognized frameworks (e.g., IEEE, ISO, White House AI Bill of Rights) to define internal policy for fairness, transparency, accountability, and privacy.
• Document these guidelines publicly to demonstrate commitment to responsible AI development — this can also bolster customer and partner trust.

C. Strengthen Data Management & Privacy Practices

1. Data Minimization & Consent

• For PHI, ensure you have robust de-identification or pseudonymization for AI training data.
• Where possible, obtain explicit patient consent for AI usage — especially for new therapy areas, to align with potential stricter EU regulations.

2. Continuous Monitoring & Auditing

• Build systematic checks for data drift, model performance degradation, and security vulnerabilities.
• Implement automated auditing pipelines that log how data is used, any transformations applied, and ensure compliance with relevant standards.

D. Prepare for AI Life Cycle & Post-Market Monitoring

1. Design & Validation

• Expand your current validation test set approach to include bias and fairness testing (e.g., outcome differences across demographics).
• Maintain clear documentation of data sources, labeling, training methodology, and known limitations of the model (aligns with FDA’s recommended “transparency of labeling” for AI devices).

2. Deployment & Monitoring

• Ensure you have robust version control for models and auditing capabilities (who trained the model, on which dataset, with what hyperparameters).
• Implement real-time monitoring for performance and a feedback loop from users/clinical staff to catch potential errors quickly.

3. Continuous Improvement & Updates

• If your AI continuously learns or updates (e.g., from new data), plan for regulatory compliance with “learning algorithms” (FDA guidance on “Predetermined Change Control Plans”).
• Document changes and re-validate models after significant updates.

E. Expand Your Compliance & Policy Expertise

1. Hire or Partner for AI-Focused Regulatory Expertise

• Given your compliance team is quite small and multitasks, consider bringing in specialized consultants or legal counsel with AI regulatory experience.
• If budget allows, a dedicated compliance lead for AI/ML can coordinate across product, legal, and engineering teams.

2. Engage with Industry Bodies & Health Regulators

• Participate in relevant industry consortia (e.g., AdvaMed, HIMSS) to stay on top of best practices and shape the conversation.
• Build relationships with the FDA and EU regulators early — especially for your AI-assisted diagnosis and therapy expansions.

3. Stakeholder Education & Training

• Educate your product and engineering teams about new AI regulations, guidelines, and best practices.
• Encourage a culture of “compliance by design,” where regulatory constraints and ethical considerations are built into the product roadmaps from the start.

3. Concrete Next Steps

1. Create a Regulatory Roadmap Document

• Outline near-term (0–6 months), mid-term (6–18 months), and long-term (18–36 months) compliance targets — especially for expansion into therapy.
• Align with EU AI Act timeline (possibly final in 2023–2024, enforcement by ~2025) and any upcoming FDA rule changes.

2. Form the AI Oversight/Compliance Committee

• Appoint leads from Product, Data Science, Security, and Legal/Compliance.
• Schedule recurring reviews for new AI models, expansions, partnerships.

2. Conduct a Gap Assessment

• Benchmark your current processes against frameworks like NIST AI RMF, draft EU AI Act, and FDA’s SaMD guidelines.
• Identify the immediate gaps in data governance, model risk management, documentation, and biases.

2. Implement or Refine an AI Lifecycle Management Platform

• Automate versioning, testing, monitoring.
• Document the entire model development pipeline from data ingestion to deployment for transparency and traceability.

3. Establish Transparent Communication Channels with Partners

• Provide periodic updates to the medical institutions and healthcare providers you partner with — demonstrate your proactive approach to AI compliance.
• Invite them to collaborate or pilot new responsible AI workflows.

Closing Thoughts

By systematically aligning with key emerging AI frameworks (NIST, FDA, EU AI Act) and enhancing your internal governance, you will be well-positioned to adapt swiftly as regulations crystallize. Beyond simply avoiding compliance pitfalls, a proactive approach can serve as a competitive differentiator — building trust with healthcare partners, patients, and regulators alike.

If you need more detailed support — such as drafting specific policies, implementing bias audits, or navigating upcoming EU AI Act conformity assessments — I can outline step-by-step processes, templates, and project plans tailored to your products and organizational structure. Let me know how else I can help!

ME: Wow, this is a lot, and it seems like I have some work to do. Can you help me understand the 10 most important specific actions or next steps to take and get us going?

ChatGPT:

Below are 10 focused actions that will help you establish an immediate roadmap and momentum for AI regulatory preparedness:

1. Form an AI Oversight Committee

• Assemble a cross-functional group (Product, Data Science, Security, Legal/Compliance, Clinical).
• Give it authority to set standards, approve new initiatives, and track compliance progress.

2. Complete a Gap Assessment

• Compare current processes against key frameworks:
• FDA SaMD guidelines, EU MDR/AI Act draft, NIST AI RMF.
• Identify specific gaps (e.g., bias testing, model documentation).

3. Develop an Internal AI Governance Policy

• Clearly state principles (e.g., fairness, privacy, accountability), requirements (documentation, monitoring), and who is responsible for each step in the AI lifecycle.

4. Create an AI Risk & Compliance Roadmap

• Outline a timeline (0–6 months, 6–18 months, 18+ months) for evolving governance, training, and policy adoption.
• Prioritize high-impact areas first (AI-assisted diagnosis, therapy expansion).

5. Implement Structured Model Documentation

• Adopt a “model card” or “factsheet” approach: track training data sources, known limitations, performance metrics, version numbers.
• Helps with audits and demonstrates compliance readiness.

6. Enhance Data Management & Security

• Strengthen PHI handling for AI (de-identification, encryption, controlled access).
• If you haven’t already, adopt robust data cataloging tools so you always know origin and consent status of each dataset.

7. Introduce Bias & Fairness Testing

• Integrate bias tests (e.g., demographic performance comparisons) into model evaluation pipelines.
• Document any issues found and mitigation strategies.

8. Expand In-House AI Regulatory Expertise

• Consider hiring an AI-focused regulatory consultant or a specialized compliance officer.
• Provide training for product teams on emerging AI regulation (FDA, EU AI Act, NIST).

9. Engage Regulators & Industry Bodies

• Join healthtech/AI committees (e.g., HIMSS, AdvaMed, or local AI consortia).
• Stay updated on evolving rules; coordinate with FDA/EU regulators if your product is classified as a medical device.

10. Partner Communication & Collaboration

• Share your AI governance roadmap with medical institutions and partners.
• Solicit their feedback and collaborate on future pilots or compliance best practices — early alignment can avert surprises later.

By focusing on these discrete steps, you’ll create a clear structure for managing AI risk, aligning with upcoming regulations, and building trust internally and externally.

ME: Given what you know, what top three risks might you be concerned with if you were in my role?

ChatGPT:

Here are three core risks you should be most alert to:

1. Regulatory Non-Compliance

• AI-assisted diagnosis or therapy tools could be deemed “medical devices,” triggering stringent FDA/EU oversight. If your products aren’t aligned with evolving AI regulations (e.g., FDA SaMD or the draft EU AI Act), you could face fines, delays, or forced product changes.

2. Data Privacy & Security Gaps

• Handling PHI and sensitive clinical data always carries HIPAA, GDPR, and potentially forthcoming AI privacy rules. Any breach or mishandling — especially if your AI systems collect or generate new types of data — can lead to reputational damage, fines, and loss of partner trust.

3. AI Bias & Ethical Failures

• Undetected biases in diagnosis or therapy recommendation models risk patient harm, legal exposure, and ethical scrutiny. Regulators and customers increasingly demand demonstrable fairness and transparency in AI outputs, which can be challenging with limited internal governance.

ME: Got it. Then jumping to step 3 in your plan, Develop an Internal AI Governance Policy, what prudent immediate actions, if any, should we take here to manage some of the risk you identified, without unduly stifling the use of and innovation with AI tools?

ChatGPT:

Below are some prudent, near-term actions to start an internal AI governance policy that addresses key risks without stifling innovation:

1. Define Clear Principles & Scope

• Scope: Spell out which AI projects and tools must follow these guidelines (e.g., anything touching PHI or clinical use).
• Principles: Frame them around patient safety, data privacy, fairness, and transparency — concise enough that teams can interpret and apply them day-to-day.

2. Adopt a Risk-Based Approach

• Classify AI products by risk (e.g., “Low,” “Medium,” “High”), factoring in direct patient impact, data sensitivity, and regulatory classification (e.g., SaMD).
• Set baseline requirements for each risk level (e.g., more stringent monitoring, documentation for “High” risk).

3. Outline Roles & Responsibilities

• Define who’s accountable for ensuring compliance (e.g., the AI Oversight Committee, Data Science leads, Product owners).
• Ensure sign-offs for high-risk models include cross-functional review (clinical, security, compliance).

4. Set Minimum Documentation Standards

• Require “model cards” or brief, standardized documentation for each AI system, including training data source, known limitations, performance metrics, and ongoing monitoring results.
• This balances innovation (quick iteration allowed) with an essential compliance record.

5. Require Basic Bias & Fairness Checks

• Introduce at least one simple test to compare model performance across demographic groups for any clinical or patient-facing AI.
• Document findings and mitigation plans if you find significant discrepancies.

6. Mandate Secure Data Handling

• Link the AI policy directly to existing HIPAA, SOC2, and GDPR security/privacy rules.
• Emphasize data minimization (only collect what’s necessary) and enforce robust encryption and access controls in all AI workflows.

7. Implement a Regular Review Cadence

• Schedule periodic check-ins (e.g., quarterly) to assess compliance, track new risks, and review any changes in regulations.
• Keep policy flexible so you can refine it as regulations or product lines evolve.

By keeping the policy relatively lean — focused on risk-tiered requirements and minimal but necessary documentation — you can manage regulatory and ethical risks without encumbering the pace of AI development.

<end conversation>