Lecture Notes: Risks and Mitigation Strategies for Generative AI

Introduction

• Generative AI, especially large language models (LLMs), is beneficial for assisting people with writing in English.
• Risks associated with LLMs include misinformation, perception of understanding, and potential hijacking by malicious actors.

Key Risks of Generative AI

1. Hallucinations

• Definition: Falsehoods or errors produced by LLMs when predicting words without understanding.
• Issue: Incorrect information is presented in a syntactically correct manner, leading to potential misinformation.
• Example: Incorrect attribution of authorship due to conflicting sources in training data.

Mitigation Strategy: Explainability

• Implement: Use inline explainability paired with real data systems (e.g., a knowledge graph) for data lineage and provenance.

2. Bias

• Issue: Outputs may reflect biases, such as listing only white male poets in response to a query.
• Mitigation Strategy:
  • Culture: Foster a culture of diversity and multidisciplinary teams to address biases.
  • Audits: Conduct pre- and post-deployment audits of AI models.

3. Consent

• Issue: Concerns regarding whether data was gathered with consent and potential copyright issues.

Mitigation Strategy: Auditing and Accountability

• Implement: Establish AI governance processes, ensure compliance with laws, and incorporate user feedback.

4. Security

• Risks: Malicious Use
  • Leaking private information, aiding phishing, spam, or scams.
  • Jailbreaking: Modifying AI to promote harmful activities.
  • Indirect Prompt Injection: Altering data in a way that changes AI behavior.

Mitigation Strategy: Education

• Environmental Impact: Training LLMs is resource-intensive (e.g., carbon footprint comparable to numerous flights).
• Education Goals:
  • Understand AI strengths, weaknesses, and environmental costs.
  • Educate on responsible curation, risks, and opportunities.

Conclusion

• Importance of education and awareness in AI use.
• Encourage inclusivity and accessibility in AI education.
• Call for diverse participation and skill sets in AI development.

Final Thoughts

• The technology is here to stay; we must define our desired relationship with AI.
• Aim to empower individuals with augmented intelligence responsibly.

• Note: Education is key to minimizing risks and promoting responsible AI usage.