Ethical Standards for Large Language Model (LLM)

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We are committed to ensuring the ethical and responsible use of Large Language Models (LLMs) in all academic and research activities. The following ethical standards will guide the development, deployment, and utilization of LLMs:

1. Mitigating Bias and Promoting Inclusivity

• Regularly review model outputs to identify and address potential biases.
• Use diverse and representative datasets during training to ensure balanced perspectives.
• Refine prompts and methodologies to mitigate skewed or discriminatory outputs.
• Prioritize content that promotes fairness and inclusivity in all LLM applications.

2. Ensuring Accuracy and Verifiability

• Implement rigorous validation processes to fact-check outputs, especially for critical or sensitive information.
• Encourage human oversight and verification for high-stakes applications such as healthcare, legal advice, or finance.
• Communicate to users that LLMs may produce information that requires further validation.

3. Respecting Privacy and Data Confidentiality

• Avoid inputting sensitive or personally identifiable information into LLMs.
• Use anonymized or aggregated data where possible and ensure compliance with data protection regulations (e.g., GDPR, HIPAA).
• Establish protocols for secure data storage, sharing, and disposal in line with AUT’s data protection policies.

4. Minimizing Environmental Impact

• Optimize computational efficiency by selecting appropriate models and reducing unnecessary resource consumption.
• Use smaller, task-specific models when applicable and adopt sustainable computing practices.
• Evaluate and implement strategies to minimize the carbon footprint of LLM training and deployment.

5. Managing Hallucinations and Misinterpretations

• Implement review mechanisms to detect and correct inaccurate or misleading outputs.
• Establish clear guidelines for human oversight in educational and research settings.
• Educate users about LLM limitations and encourage critical evaluation of generated content.

6. Respecting Intellectual Property and Data Ownership

• Ensure compliance with intellectual property laws and secure necessary licenses for data use.
• Maintain transparency about data sources, usage, and ownership to build trust and accountability.
• Avoid using copyrighted material without explicit permission and establish a clear data provenance policy.

7. Addressing Security and Safety Concerns

• Implement robust security measures to safeguard against adversarial attacks and misuse.
• Monitor for inappropriate use of LLMs to generate malicious content such as misinformation or offensive language.
• Develop protocols to respond to security breaches or malicious activities effectively.

8. Promoting Ethical and Transparent Use

• Develop and enforce ethical guidelines that align with AUT’s values and legal requirements.
• Engage with diverse stakeholders, including ethicists, legal experts, and community representatives.
• Promote transparency in how LLMs are trained, tested, and deployed, and disclose model limitations and biases.

9. Educating and Empowering Users

• Provide training and resources to help users understand LLM capabilities, limitations, and ethical considerations.
• Encourage critical thinking and ethical awareness in research and educational contexts.
• Facilitate discussions and workshops to address emerging ethical concerns and promote best practices.

10. Final Recommendations

• Experiment with different prompts to optimize LLM performance while maintaining ethical considerations.
• Test and validate all outputs, especially for critical applications or sensitive information.
• Use LLMs to enhance productivity, but apply critical thinking to generated outputs.

By adhering to these ethical standards, we aim to foster a responsible, transparent, and inclusive environment for the use of LLMs in academic research, teaching, and innovation, ensuring that the benefits of these advanced technologies are realized while minimizing potential risks and ethical challenges.