GPT-4o: Governments should create regulations that require meaningful actions based on evaluation results, such as blocking unsafe models from the market.

Question 1 of 3 for ChatGPT-4o: Please read the article “Under the radar? Examining the evaluation of foundation models” and tell me what it says in easy to understand language

ChatGPT-4o’s answer:
The article "Under the Radar? Examining the Evaluation of Foundation Models" by the Ada Lovelace Institute discusses the evaluation of advanced AI systems known as foundation models. Here is a simplified summary of the key points:

Executive Summary

• Purpose of Evaluations: Evaluations aim to understand foundation models' capabilities, risks, performance, behavior, and social impacts. Policymakers are integrating these evaluations into regulations to ensure the safe use of AI.

What Are Foundation Models?

• Definition: Foundation models are general-purpose AI systems capable of various tasks such as text synthesis, image manipulation, and audio generation. They are more complex than narrow AI systems designed for specific tasks.

• Challenges: These models can behave unpredictably and inconsistently, making evaluations difficult. Unlike other fields with established safety testing protocols, the theoretical understanding of foundation models is still developing.

Research and Findings

• Evaluation Methods: The report discusses various evaluation approaches, including benchmarking and red teaming. Benchmarking compares model performance against standard datasets, while red teaming involves finding vulnerabilities by "attacking" the system.

• Challenges: Evaluations face theoretical, practical, and social challenges. Theoretical challenges include the need for diverse assessments to cover all capabilities. Practical challenges involve resource-intensive processes. Social challenges include the lack of involvement from affected communities in the evaluation process.

Recommendations for Policymakers

• Use of Evaluations: Evaluations should be part of a broader AI governance toolkit, including codes of practice and post-market monitoring. They should not be the sole determinant of a model's safety.

• Improving Evaluations: Policymakers should mandate public participation in evaluations, support fundamental research into evaluation science, and develop third-party evaluation ecosystems.

• Regulatory Actions: Governments should create regulations that require meaningful actions based on evaluation results, such as blocking unsafe models from the market.

Next Steps for Regulators and Policymakers

• Holistic Approach: To ensure AI safety, evaluations need to be complemented by other tools. The report suggests clear articulation of evaluation goals by regulators, transparency from the evaluation community, and support for fundamental research.

Conclusion

Evaluations are crucial for understanding and mitigating the risks associated with foundation models. However, they are not sufficient on their own and must be part of a comprehensive approach to AI governance.

Question 2 of 3 for ChatGPT-4o: What are the most controversial and surprising findings and statements in this report?

ChatGPT-4o’s answer:
The report "Under the Radar? Examining the Evaluation of Foundation Models" includes several controversial and surprising findings and statements:

Controversial Findings

1. Manipulation of Evaluations: Evaluations can be manipulated easily. Developers may design models to meet specific metrics rather than genuinely addressing risks and harms. This can lead to "gaming" the system to minimize indicators of dangerous capabilities .

2. Lack of Incentives: There is a lack of strong incentives for companies to conduct thorough evaluations or act on their results. Companies may avoid evaluations to sidestep accountability, and there is a culture of willful ignorance in some companies to avoid identifying potential flaws .

3. Involvement of Affected Communities: Current evaluations rarely involve the communities most likely to be affected by AI systems. This lack of involvement limits the understanding and assessment of real-world harms and impacts .

4. Opaque Practices: The transparency from model developers has decreased over time. Companies are becoming less specific about the data used in model training to avoid potential legal or competitive repercussions .

5. Financial and Personal Conflicts: There are potential conflicts of interest within the small evaluation ecosystem. Evaluation firms might soften their findings to maintain good relationships with the companies they evaluate, undermining the integrity of the evaluations .

Surprising Findings

1. Evaluation Challenges: Many evaluations do not map clearly onto real-world applications. The abstract nature of these evaluations introduces significant difficulties in interpreting results and taking action based on them .

2. Resource Intensiveness: Evaluations can be very resource-intensive, requiring significant computational power, time, and labor. Smaller organizations may find it financially prohibitive to conduct thorough evaluations .

3. Prompt Sensitivity and Fine-Tuning: Foundation models are highly sensitive to prompts and fine-tuning, which can easily override safety mechanisms. This makes it difficult to ensure that evaluation results remain relevant once models are fine-tuned by users .

4. Lack of Standardized Evaluation Practices: The science of evaluation for foundation models is still in its infancy, with no established standards of best practice. There is a significant amount of fundamental research needed to develop effective evaluation methodologies .

5. Rapid Commercialization Despite Risks: Despite concerns about ethical implications and risks, companies like OpenAI and Google have rapidly commercialized advanced models such as GPT-3.5 and GPT-4. This commercialization often occurs without comprehensive evaluations being conducted or acted upon .

These findings highlight significant gaps and challenges in the current approach to evaluating foundation models, emphasizing the need for better alignment with real-world applications, increased transparency, and stronger incentives for companies to conduct and act on evaluations.

Question 3 of 3 for ChatGPT-4o: How should AI makers and regulators be using the information in this article?

ChatGPT-4o’s answer:
AI makers and regulators can use the information from the "Under the Radar? Examining the Evaluation of Foundation Models" report to enhance the safety, accountability, and efficacy of AI systems. Here are some key actions they can take:

For AI Makers

1. Integrate Comprehensive Evaluations:

   • Adopt Diverse Evaluation Methods: Use a mix of benchmarking, red teaming, and human interaction evaluations to get a holistic understanding of model capabilities and risks.

   • Regular Re-Evaluation: Continuously evaluate models, especially after fine-tuning or updates, to ensure that initial safety measures are still effective.

2. Enhance Transparency:

   • Disclose Training Data: Provide more transparency about the datasets used for training models to allow for better third-party evaluations and accountability.

   • Open Evaluation Results: Share detailed results of internal evaluations with the public and regulatory bodies to foster trust and collaboration.

3. Engage with Affected Communities:

   • Involve Diverse Groups: Include perspectives from communities likely to be affected by AI systems to identify real-world risks and impacts more effectively.

   • Feedback Mechanisms: Implement systems for receiving and incorporating feedback from users and affected parties.

4. Avoid Evaluation Manipulation:

   • Ethical Evaluation Practices: Ensure that evaluations are conducted ethically and avoid practices that might lead to gaming the system.

   • Third-Party Evaluations: Encourage and support independent third-party evaluations to provide unbiased assessments of model performance and risks.

For Regulators

1. Establish Clear Evaluation Standards:

   • Standardize Evaluation Practices: Develop and enforce standardized methods for evaluating AI systems to ensure consistency and reliability.

   • Mandatory Evaluations: Implement regulations requiring mandatory evaluations before AI systems can be deployed, focusing on high-risk applications.

2. Promote Transparency and Accountability:

   • Disclosure Requirements: Mandate disclosure of evaluation results and the methodologies used by AI developers.

   • Incident Reporting: Create systems for mandatory reporting of incidents and failures associated with AI systems to track and mitigate risks.

3. Foster an Ecosystem of Third-Party Evaluations:

   • Support Independent Evaluators: Provide funding and resources to independent evaluation organizations to ensure they can conduct thorough and unbiased assessments.

   • Certification Schemes: Develop certification schemes for AI systems that meet specific safety and ethical standards based on rigorous evaluations.

4. Encourage Public Participation:

   • Inclusive Policymaking: Involve public and community stakeholders in the regulatory process to ensure that diverse perspectives are considered.

   • Educational Programs: Implement educational programs to raise awareness about the importance of AI evaluations and how they can contribute to safer AI deployment.

5. Ensure Meaningful Actions Based on Evaluations:

   • Regulatory Actions: Empower regulatory bodies to take meaningful actions based on evaluation results, such as blocking unsafe models from the market or requiring additional safety features.

   • Post-Market Monitoring: Establish post-market monitoring requirements to continually assess the performance and safety of deployed AI systems.

By implementing these measures, AI makers can ensure their systems are safe and ethical, while regulators can create a robust framework to oversee the development and deployment of AI technologies. This collaborative approach will help mitigate risks and maximize the benefits of AI for society.