GPT-4o: AI makers should grant auditors access to the data and models used in the AI system. AI makers need to provide detailed documentation of the AI system's components, including data sources.

Question 1 of 3 for ChatGPT-4o: please read the report “Global Approaches to Auditing Artificial Intelligence” and tell me what it says in easy to understand language.

ChatGPT-4o’s answer:
The report titled "Global Approaches to Auditing Artificial Intelligence" focuses on understanding how different organizations, governments, and industries are conducting audits of AI systems to ensure they are safe, ethical, and in line with established standards. Here's a simplified overview of what it covers:

1. Why AI Audits Matter: AI systems can be complex and sometimes cause unintended negative effects. To manage these risks, audits are performed to check if AI systems behave as expected and meet ethical and safety guidelines. These audits are becoming a global trend to ensure AI is used responsibly.

2. Types of AI Audits:

   • Internal Audits: Performed by the company or organization using the AI system. These audits focus on ensuring that the AI system works correctly within the organization’s policies.

   • External Audits: Conducted by third-party auditors who check whether the AI system is functioning ethically and meeting regulatory standards.

   • Community Audits: Sometimes, users or external communities are involved in auditing AI systems to assess their impact on society.

3. Challenges in Auditing AI: Auditors often face difficulties in getting access to the necessary data and models that make up an AI system. For a full audit, auditors need to understand how the AI system works, the data it uses, and how decisions are made. Transparency and access to this information are key issues.

4. Global Reach: Most audits so far have been done in wealthier regions like North America and Europe. However, AI systems impact people globally, so auditing efforts need to be expanded to account for worldwide effects, including in developing countries.

5. Recommendations:

   • Building Trust: Auditors, whether internal or external, need to work together to create a trustworthy ecosystem of audits. This will help ensure that audits are effective and reliable.

   • Access to Information: Auditors need better access to the data and inner workings of AI systems to make accurate assessments.

   • Global Standards: To ensure that audits are consistent and comparable worldwide, there needs to be a global standard for how AI audits are conducted. These standards would help reduce the risk of harm from AI systems and ensure they are safe for all users.

In summary, the report highlights the growing importance of auditing AI systems, the need for more transparency, and the global efforts required to create a trustworthy system for checking the safety and ethics of AI technologies.


Question 2 of 3 for ChatGPT-4o: List all benefits relevant to the three types of audits and what to look out for when conducting them.

ChatGPT-4o’s answer:
Here’s a breakdown of the benefits and considerations for the three main types of AI audits—internal, external, and community audits—based on the report:

1. Internal Audits

• Benefits:

  • Access to Data and Systems: Internal auditors have full access to the organization's AI systems, including data, models, and documentation, which allows for a thorough and detailed evaluation.

  • Alignment with Organizational Goals: These audits can be designed to ensure that the AI systems align with the organization's policies, strategies, and risk management practices.

  • Ongoing Monitoring: Internal audits can be conducted regularly throughout the AI system's lifecycle, allowing for continuous improvement and compliance.

• What to Look Out For:

  • Bias: Check for unintended biases in AI outputs, particularly in areas like hiring, healthcare, and criminal justice, where AI decisions can affect people's lives.

  • Compliance with Policies: Ensure the AI system is in line with internal policies and regulatory requirements.

  • Ethics and Accountability: Review whether the system's design and operations meet ethical standards and corporate social responsibility goals.

  • Over-reliance on Self-regulation: Internal audits might miss key issues due to potential conflicts of interest or pressure from within the organization.

2. External Audits

• Benefits:

  • Independent Perspective: External auditors provide an unbiased evaluation, which can increase trust and credibility for regulators, customers, and other stakeholders.

  • Regulatory Compliance: External audits often focus on compliance with legal frameworks and industry standards, helping organizations avoid legal issues and fines.

  • Specialized Expertise: External auditors often have specialized knowledge and experience in auditing complex AI systems, which can lead to a more thorough and accurate assessment.

• What to Look Out For:

  • Access to Information: External auditors may face limitations in accessing proprietary data or systems, which could restrict the depth of their analysis.

  • Transparency: Ensure clear communication between the organization and auditors, and provide the necessary documentation for an accurate audit.

  • Global Standards: Verify that the audit follows relevant global standards and frameworks, especially if the AI system is used across different countries or industries.

3. Community Audits

• Benefits:

  • User-centric Evaluation: Community audits focus on how AI systems impact real users, particularly marginalized or affected groups, offering insights into real-world social impacts.

  • Social Accountability: By involving communities in the audit process, companies can demonstrate their commitment to ethical AI and social responsibility.

  • Identifying Hidden Issues: Community audits can reveal biases, harmful effects, or overlooked risks that may not be apparent in internal or external audits, particularly in diverse user populations.

• What to Look Out For:

  • Inclusive Engagement: Ensure that the audit includes voices from all relevant communities, especially those most likely to be affected by the AI system.

  • Qualitative Insights: Focus on both quantitative and qualitative data to understand the AI system's impact on user experience and social dynamics.

  • Legal and Ethical Boundaries: Make sure the audit complies with legal and ethical standards, especially when involving user data or feedback.

General Considerations Across All Audits:

• Data and Model Access: Auditors need access to the underlying data and models used in AI systems to evaluate potential biases, risks, and performance.

• Global Reach: Since AI systems often operate in a global context, audits should consider the worldwide impact of AI, including its effects on marginalized communities and environmental sustainability.

• Comprehensive Documentation: Ensure proper documentation of AI processes, including how decisions are made, to provide transparency and accountability for auditors.

• Standardization: Auditors should use consistent frameworks and standards to ensure the reliability and comparability of audit results across different systems and regions.

These audits, when used together, provide a more robust and trustworthy approach to evaluating AI systems, identifying risks, and ensuring ethical AI deployment.


Question 3 of 3 for ChatGPT-4o: What do AI makers need to do to ensure that these audits can be conducted in a robust way and are there any recommendations for regulators on the basis of the information in this report?

ChatGPT-4o’s answer:
To ensure that AI audits are conducted in a robust and effective manner, AI makers (developers and deployers of AI systems) need to take several steps. Additionally, the report provides recommendations for regulators to improve the audit ecosystem. Here's a summary:

What AI Makers Need to Do:

1. Ensure Data and System Transparency:

   • Comprehensive Documentation: AI makers need to provide detailed documentation of the AI system's components, including data sources, models, algorithms, and decision-making processes. This will allow auditors to understand how the system works and evaluate its performance.

   • Access to Data and Models: AI makers should grant auditors access to the data and models used in the AI system. This is critical for evaluating the system’s biases, risks, and performance. Without this, audits can only be partial and may miss key issues.

   • Explainability and Interpretability: AI systems should be designed with features that make their operations explainable and interpretable to non-experts. This helps auditors assess the system’s decisions and outcomes more effectively.

2. Support Auditability from the Start:

   • Audit Trails: Implement systems to record and track AI decisions (e.g., logs or audit trails) that document how AI models make decisions. This evidence is crucial for auditors to evaluate the system’s operations.

   • Incorporate Governance Mechanisms: AI makers need to implement internal governance mechanisms that ensure compliance with ethical guidelines, regulatory standards, and organizational policies. These structures should be designed to facilitate both internal and external audits.

3. Collaborate with Auditors:

   • Engagement with Independent Auditors: AI developers should engage external auditors early and regularly throughout the AI system’s lifecycle. Independent audits provide objectivity and can reveal hidden risks or issues.

   • Involve Community Stakeholders: Collaborate with affected communities and user groups to conduct audits that consider the real-world impact of AI systems on diverse populations. This ensures that the audit reflects broader societal concerns.

4. Mitigate Known Risks:

   • Bias and Fairness Checks: Regularly audit AI systems for potential biases and discriminatory behavior, especially in applications that can significantly impact people's lives (e.g., hiring, criminal justice, healthcare). These audits should assess whether the system produces fair outcomes across different demographic groups.

   • Security and Robustness: Implement measures to make AI systems robust against adversarial attacks and security risks. Continuous monitoring and security audits should ensure the system remains safe over time.

5. Disclose Audit Results:

   • Public Reporting: AI makers should commit to public disclosure of audit findings to build trust with stakeholders. Transparency around the auditing process and its results demonstrates accountability and trustworthiness.

   • Commit to Continuous Improvement: Use audit findings to improve the AI system's design, governance, and performance. This process should be iterative, with ongoing audits leading to incremental improvements.

Recommendations for Regulators:

1. Create Global Standards for AI Audits:

   • Standardization: Regulators should work with international bodies to develop global standards for AI auditing. These standards would establish clear criteria and benchmarks for what AI audits should evaluate, making audit results comparable across industries and regions.

   • Harmonized Frameworks: Since AI systems often operate globally, regulators need to harmonize auditing frameworks across jurisdictions to ensure consistent oversight and governance.

2. Mandate Audits for High-risk AI Systems:

   • High-risk Applications: Regulators should require mandatory audits for AI systems that are deemed "high-risk" (e.g., autonomous vehicles, healthcare, finance, law enforcement). These audits should focus on ensuring the safety, fairness, and compliance of AI systems in sensitive domains.

   • Continuous Monitoring: For high-risk AI systems, regulators could mandate post-market monitoring audits to ensure ongoing compliance and risk mitigation after the system is deployed.

3. Enforce Documentation and Transparency Requirements:

   • Auditability Requirements: Regulators should enforce legal requirements for AI makers to provide the necessary documentation and transparency for audits. This includes access to data, models, and algorithms to ensure that auditors can assess the system comprehensively.

   • Registry of AI Systems: Establish a registry for high-risk AI systems (similar to the EU’s AI Act) that requires AI developers to register their systems and make audit results publicly available. This increases accountability and makes it easier for regulators to track AI systems.

4. Support Independent Audits:

   • Legal Protections for Auditors: Regulators should create legal protections for independent auditors to conduct adversarial audits (e.g., red-teaming) without fear of legal repercussions, such as safe harbor provisions for those conducting audits on AI systems used in sensitive areas.

   • Funding for Community-led Audits: Regulators should support the creation of community and user-led audits by providing resources and funding to allow impacted communities to conduct their own assessments of AI systems. This can help identify social harms or biases that traditional audits might miss.

5. Incentivize Ethical and Responsible AI:

   • Certification and Labeling: Regulators could develop certification systems for AI systems that meet certain ethical and regulatory standards based on audit results. Certified systems could be given a "trustworthiness" label, providing market incentives for companies to comply with high standards.

   • Accountability Mechanisms: Introduce legal accountability mechanisms, such as fines or penalties, for companies that fail to conduct proper audits or whose systems are found to have significant harmful impacts.

By implementing these recommendations, both AI makers and regulators can create a robust and trustworthy auditing ecosystem that ensures AI systems are safe, ethical, and transparent.