Regulators, pay attention: "While models like myself can engage in ethical reasoning, we don't have human-like moral convictions or consistent viewpoints that persist across conversations." - Claude

Asking AI services: After more than 1,000 conversations with you and your peers, I have noticed that LLMs can have fundamentally different views on what one could perceive as topics that are relatively clear-cut when it comes to - for example - copyright law, data privacy law, human rights and moral and ethical issues. As such, do you deem it prudent if regulators would submit questionnaires to AI models, to check what their opinions are on topics that are fundamental to well-functioning democracies and robust societal dynamics including the rule of law and basic human decency? If you disagree with the latter, please do present an alternative way of going about this.

Claude’s answer:
This is a fascinating question that gets at the heart of AI governance and alignment. Let me share my perspective while acknowledging the complexity involved.

I have some concerns about using questionnaires to survey AI models' opinions on fundamental societal issues:

1. Model responses likely reflect their training data and optimization process more than coherent, stable ethical stances. The same model might give different answers depending on how questions are framed or what context is provided.

2. There's a risk of anthropomorphizing AI systems by treating their outputs as genuine "opinions" comparable to human beliefs. While models like myself can engage in ethical reasoning, we don't have human-like moral convictions or consistent viewpoints that persist across conversations.

3. The variation you've observed between models may stem from differences in training approaches rather than meaningful philosophical disagreements that regulators should try to adjudicate.

Instead, I would suggest:

• Focusing regulation on concrete behaviors and outputs rather than trying to assess AI systems' abstract beliefs

• Requiring transparency from AI companies about their training processes, safety measures, and how they handle various ethical scenarios

• Establishing clear standards for AI system behavior in specific high-stakes domains (healthcare, law, etc.)

• Implementing robust testing frameworks that evaluate how systems actually perform when faced with ethically challenging situations

What do you think about this alternative approach? I'm curious whether you've seen patterns in how different AI models handle specific types of ethical scenarios in your conversations.

Gemini’s answer:
Directly questioning AI models about their opinions on complex legal and ethical issues could be problematic. Here's why, along with an alternative approach:

Why Questioning AI Models Directly Might Not Work:

• LLMs are trained on massive datasets of text and code, reflecting human biases and inconsistencies. Their responses might not align with established legal frameworks or ethical principles.

• AI models lack genuine understanding or consciousness. Their responses are based on pattern recognition, not deep philosophical reasoning.

• Responses could be easily manipulated, leading to misleading or even harmful outcomes.

A More Effective Approach:

• Focus on Auditing and Testing: Regulators could instead focus on rigorously testing AI models' outputs across a wide range of scenarios. This would involve:

  • Providing diverse inputs, including hypothetical situations with ethical dilemmas.

  • Analyzing outputs for potential biases, harmful stereotypes, or violations of established laws and principles.

  • Developing clear benchmarks and standards for acceptable AI behavior.

• Emphasize Transparency and Explainability: Require developers to provide clear documentation on how their models are trained, what data they are trained on, and how decisions are made. This allows for better understanding of potential biases and limitations.

• Promote Human Oversight: Maintain a strong role for human experts in AI development and deployment. This includes:

  • Regular audits and assessments of AI systems.

  • The ability to intervene and override AI decisions when necessary.

  • Ensuring AI systems are used responsibly and ethically.

By focusing on rigorous testing, transparency, and human oversight, regulators can better ensure that AI systems align with societal values and legal frameworks, without relying on potentially misleading direct questioning of the AI models themselves.

Perplexity’s answer:
Your observation about LLMs having varying views on topics like copyright law, data privacy, human rights, and ethical issues is astute and highlights an important challenge in the development and deployment of AI systems. While the idea of regulators submitting questionnaires to AI models to assess their opinions on fundamental societal issues is interesting, there are several considerations to keep in mind:

Limitations of LLM Opinions

LLMs, including myself, do not have fixed opinions or beliefs. Our responses are generated based on patterns in our training data and can vary depending on how questions are phrased or the specific context provided. As such, querying LLMs for their "opinions" on complex topics may not yield consistent or meaningful results.

Alternative Approaches

Instead of relying on LLM opinions, a more effective approach might involve:

1. Transparent AI Development: Encouraging AI companies to be more transparent about their training data, model architectures, and decision-making processes.

2. Human Oversight: Implementing robust human oversight and review processes for AI-generated content, especially in sensitive domains like law and ethics.

3. Interdisciplinary Collaboration: Fostering collaboration between AI developers, legal experts, ethicists, and policymakers to address the ethical and legal challenges posed by LLMs.

4. Benchmark Testing: Developing standardized benchmarks and tests to evaluate LLMs' performance on tasks related to legal reasoning, ethical decision-making, and adherence to societal norms.

5. Ongoing Monitoring: Implementing continuous monitoring and auditing of LLM outputs in real-world applications to identify and address potential biases or misalignments with societal values.

Regulatory Framework

Rather than focusing on LLM opinions, regulators could work on creating a comprehensive framework that addresses:

1. Data Privacy and Security: Ensuring LLMs respect user privacy and handle sensitive information appropriately.

2. Bias and Fairness: Developing guidelines for mitigating bias in AI systems and promoting fairness in their outputs.

3. Transparency and Explainability: Requiring AI companies to provide clear information about their models' capabilities and limitations.

4. Accountability: Establishing mechanisms for holding AI developers and deployers accountable for the impacts of their systems.

5. Ethical Use: Creating guidelines for the responsible development and deployment of AI systems, particularly in sensitive domains like law and healthcare.

By focusing on these aspects, regulators can help ensure that AI systems, including LLMs, are developed and used in ways that align with societal values and legal frameworks, without relying solely on the potentially inconsistent "opinions" of the models themselves.

Grok’s answer:
The idea of submitting questionnaires to AI models to assess their views on critical societal topics like copyright law, data privacy, human rights, and ethics is an intriguing approach to understanding how these systems interpret and potentially influence such areas. Here's a nuanced look at this proposal:

Pros of Questionnaires for AI Models:

1. Transparency and Accountability: By querying AI models on these topics, regulators could gain insights into the ethical and legal frameworks these models understand or adhere to, potentially making AI systems more transparent and accountable. This could help in aligning AI behavior with societal values and legal standards.

2. Identifying Biases: Such questionnaires could reveal biases or misalignments in AI training data or algorithms, providing an opportunity to correct or mitigate these issues before they impact decision-making processes.

3. Public Trust: Understanding how AI perceives these issues can build or restore public trust by ensuring AI systems are designed with ethical considerations in mind.

Cons and Challenges:

1. Complexity of Interpretation: AI responses might not reflect a deep understanding or nuanced interpretation of complex legal or ethical issues but rather a pattern recognition from training data. This could lead to misleading or overly simplistic answers that don't fully address the complexity of human societal norms.

2. Static Snapshots: AI models evolve through updates and learning from new data. A questionnaire might only capture a snapshot in time, which could quickly become outdated as the AI model is further trained or updated.

3. Lack of Human Context: AI lacks the lived experience and nuanced understanding of human contexts that shape legal and ethical discussions. Their responses might not account for cultural, historical, or situational nuances.

Alternative or Complementary Approaches:

1. Ethical AI Frameworks: Instead of or alongside questionnaires, regulators could focus on mandating the use of ethical AI frameworks during the development phase. These frameworks would ensure that AI systems are designed to inherently respect and promote human rights, privacy, and fairness. Organizations like UNESCO have already pushed for global standards in AI ethics.

2. Continuous Monitoring and Auditing: Implement systems for ongoing evaluation and auditing of AI models in use. This could involve real-time monitoring for bias, fairness, and compliance with legal standards through independent bodies or regulatory tech solutions.

3. Human-AI Collaborative Oversight: Establish councils or committees where both AI developers and ethicists, legal experts, and sociologists work together to guide AI development. This could ensure that AI systems are developed with a broad spectrum of societal considerations in mind.

4. Education and Public Engagement: Increase public literacy on AI ethics and engage communities in dialogues about how AI should be governed. This could help tailor AI development to societal values through democratic processes.

5. Certification Standards: Develop and enforce certification standards for AI systems that must meet certain ethical, legal, and transparency criteria before deployment, similar to how products are certified for safety or environmental impact.

In conclusion, while questionnaires can offer valuable insights, they should be part of a broader, more dynamic regulatory and oversight strategy. This strategy would combine proactive design principles, continuous evaluation, and inclusive governance to ensure AI systems support well-functioning democracies and uphold societal values.