Generative AI

GenAI poses both opportunities and challenges to teaching and learning. 

• The Academic Senate provides GenAI guidance and resources for Teaching and Learning to instructors.
• Students should remember that the UCLA Student Conduct Code applies to GenAI, and states that “Unless otherwise specified by the faculty member, all submissions…must either be the Student’s own work, or must clearly acknowledge the source.” Students should consult with their instructors about the acceptable use of GenAI for each course.
• Instructors are encouraged to clarify and communicate expectations about any acceptable uses of GenAI to their students. Instructors should contact the Teaching and Learning Center [consult@teaching.ucla.edu] to discuss their specific needs.

Ensuring the confidentiality and security of information used and generated through GenAI applications is critical for the campus community. GenAI tools should be designed in ways that maximize privacy and security of persons and personal data.

Privacy:

• Obtaining explicit and informed consent from individuals before collecting, processing, or using their data in GenAI systems. 
• Wherever possible, use anonymized datasets to minimize privacy risks, especially when working with personal or sensitive information.

Safety and Security: 

• Follow ethical guidelines for GenAI development that consider the impact of GenAI systems on privacy, fairness, and society at large.
• Use secure AI development practices among researchers and developers within the university.
• Encrypt sensitive data both at rest and in transit to protect it from unauthorized access.
• Implement strict access controls, such as Multi-factor Authentication (MFA) to ensure that only authorized personnel can access sensitive data and AI resources.
• Refer to the Available GenAI Tools matrix to verify the data classifications allowed for each available GenAI tool.
• Refrain from entering any sensitive data into GenAI tools unless the tool has been specifically cleared for P3-P4 classified data.

• GenAI systems should be regularly evaluated for bias and fairness to ensure that they do not perpetuate discrimination or harm.
• There should be transparency in how GenAI systems make decisions, especially if these decisions affect personal information.
  • AI systems should be used to enhance positive social change and encourage sustainability and environmental responsibility of GenAI systems.

• Inaccuracy: The content generated by GenAI tools can contain “hallucinations”, not be accurate, and/or be out of date. Additionally, the underlaying models powering GenAI tools may have been trained with biased or partial/incomplete data. Always use your judgement to evaluate the content generated by these tools.
• Human In The Loop & Accountability
  • GenAI tools have many limitations and risks. It is the users’ responsibility and accountability to get informed about these limitations and risks, closely review the outputs of GenAI tools, and apply their judgements before using any of these outputs.
  • Personnel developing GenAI models at UCLA need to take responsibility for any decisions that are made based on the model.
• Intellectual Property Rights Infringement: GenAI tools are trained with vast amounts of data. Some of these tools may have been trained with data without their owners consent. Always use commercially licensed GenAI tools to minimize the risks of intellectual property rights infringement.
• Bias and Discrimination: The datasets used to train GenAI tools may have incorporated biased or incomplete data, potentially causing their outputs to also generate biased and/or discriminatory content.
• Source Transparency: Some GenAI tools provide a list or links to the sources used when generating content. However, this is not true for all GenAI tools. Always review whether “true sounding” statements are based on facts or not.