aiSafety

Study Plan for Generative AI Safety

Introduction

Generative AI models like ChatGPT and DALL-E 2 have revolutionized how we interact with technology, offering unprecedented capabilities in creating text, images, and other forms of content. However, these advancements come with potential risks that need careful consideration. This learning path will guide you through essential topics in Generative AI safety, drawing from a variety of sources to provide a comprehensive understanding of the field. To create this guide, a multi-step research process was conducted, involving the examination of research papers, blogs, and articles on AI safety. Specific keywords and search strategies were used to identify relevant sources and ensure the comprehensiveness of the information presented.

Why Generative AI Safety Matters

Generative AI models, while powerful, can exhibit unintended behaviors with potentially harmful consequences. These include:

• Generating harmful or misleading content: AI models can be exploited to create deepfakes, spread misinformation, or produce offensive material1.

• Data breaches and privacy violations: AI models trained on sensitive data can leak confidential information or be used for malicious purposes3.

• Bias and discrimination: AI models can perpetuate existing societal biases, leading to unfair or discriminatory outcomes2.

• Lack of transparency and explainability: It can be difficult to understand how AI models arrive at their outputs, making it challenging to identify and address potential issues6.

Understanding these risks is crucial for developing and deploying Generative AI responsibly.

Core Areas of Study

This curriculum focuses on six key areas:

1. Foundational Research Papers

Start by exploring seminal research papers that lay the groundwork for understanding AI safety. These papers provide a theoretical foundation and insights into key challenges in the field.

| Paper Title | Key Takeaways | Summary |

| :---- | :---- | :---- |

| Training language models to follow instructions with human feedback 7 | Explores techniques for aligning language models with human intentions. | This paper investigates how to train large language models (LLMs) to effectively follow human instructions. It highlights the importance of aligning LLMs with human values and preferences to ensure they are used responsibly and beneficially. |

| Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI 7 | Provides a comprehensive overview of explainable AI and its role in responsible AI development. | This paper explores the concept of explainable AI (XAI) and its significance in building trust and understanding in AI systems. It discusses various XAI methods and their applications in different domains, emphasizing the importance of transparency and interpretability in responsible AI development. |

| A Survey on Bias and Fairness in Machine Learning 7 | Examines the issue of bias in machine learning and discusses methods for promoting fairness. | This paper provides a comprehensive overview of bias and fairness in machine learning. It explores different types of biases that can arise in AI systems and their potential impact on individuals and society. It also discusses various techniques for mitigating bias and promoting fairness in AI development and deployment. |

| On the Dangers of Stochastic Parrots: Can Language Models Be Too Big7? | Discusses the potential risks associated with large language models and the importance of responsible scaling. | This paper raises concerns about the potential risks of large language models (LLMs), particularly those trained on massive datasets. It argues that while LLMs exhibit impressive capabilities, they can also perpetuate biases, generate harmful content, and be misused for malicious purposes. It emphasizes the need for responsible scaling of LLMs and careful consideration of their ethical and societal implications. |

| From local explanations to global understanding with explainable AI for trees 7 | Explores methods for interpreting and understanding the decision-making processes of tree-based AI models. | This paper focuses on explainable AI (XAI) for tree-based models, which are widely used in various applications. It presents methods for interpreting the decision-making processes of these models, providing insights into how they arrive at their outputs. This understanding is crucial for ensuring transparency and accountability in AI systems. |

| Computational Safety for Generative AI: A Signal Processing Perspective 8 | Presents a mathematical framework for assessing and mitigating safety challenges in Generative AI. | This paper introduces a novel framework for evaluating and mitigating safety risks in Generative AI, drawing from signal processing theory and methods. It proposes a quantitative approach to assess the safety of AI models, focusing on detecting malicious inputs and harmful outputs. This framework provides a valuable tool for researchers and developers working on AI safety. |

2. Blogs and Articles on Safety Concerns

Blogs and articles offer valuable insights into real-world safety concerns and potential solutions, supplementing the theoretical foundation provided by research papers.

| Blog/Article Title | Key Takeaways |

| :---- | :---- |

| Generative AI Security Risks: 8 Critical Threats You Should Know 9 | Outlines eight key security risks associated with Generative AI, including data breaches, misuse, and model theft. |

| Oops! 5 Serious Gen AI Security Mistakes to Avoid 10 | Discusses common security mistakes in Generative AI and provides solutions for mitigating them. |

| Disrupting Cybercrime Abusing Gen AI 11 | Examines how cybercriminals exploit Generative AI and highlights the importance of robust security measures. |

| Security Risks of Generative AI and Countermeasures 3 | Provides an overview of security risks and countermeasures for Generative AI, including data leakage and inappropriate output. |

| Security Risks of Generative AI 12 | Discusses the potential for Generative AI to be used for malicious purposes and highlights the need for robust security measures. |

3. Understanding Specific Risks in Generative AI

Deepen your knowledge by focusing on specific types of risks associated with Generative AI:

• Bias: AI models can inherit and amplify biases present in their training data, leading to discriminatory outcomes. This can have significant societal implications, perpetuating existing inequalities and unfair treatment2. For example, a facial recognition system trained on a dataset with limited representation of certain ethnicities might exhibit lower accuracy for those groups.

• Misinformation: Generative AI can be used to create and spread misinformation, posing a threat to public discourse and trust in information sources. Deepfakes, synthetic media that convincingly alters reality, can be used to manipulate public opinion or damage reputations1.

• Misuse: Generative AI can be exploited for malicious purposes, such as generating harmful content, conducting phishing attacks, or creating sophisticated malware. This highlights the need for robust security measures and ethical guidelines to prevent the misuse of AI technologies1.

4. Mitigation Techniques

Learn about techniques and methods used to mitigate safety risks in Generative AI:

• Adversarial training: This technique involves training AI models on adversarial examples, which are inputs designed to fool the model. By exposing the model to these examples, it becomes more robust against malicious inputs and attacks1.

• Data anonymization and encryption: Protecting sensitive data used in training and deploying AI models is crucial. Anonymization techniques remove or obfuscate personally identifiable information, while encryption makes data unreadable to unauthorized users1.

• Human-in-the-loop systems: Incorporating human oversight in AI systems ensures responsible decision-making. This can involve human review of AI outputs, intervention in critical decisions, or ongoing monitoring of AI behavior13.

• Explainable AI (XAI): Developing AI models that are transparent and interpretable makes it easier to identify and address potential issues. XAI methods provide insights into the decision-making processes of AI models, allowing for better understanding and trust5.

5. Ethical Considerations

Explore the ethical considerations surrounding Generative AI development and deployment:

• Fairness: Ensuring AI systems are fair and unbiased is crucial to avoid discrimination against individuals or groups. This requires careful consideration of the data used to train AI models, the algorithms employed, and the potential impact of AI systems on different populations14.

• Transparency: Promoting transparency in AI systems builds trust and allows users to understand how these systems work and what data they use. This includes providing clear explanations of AI decision-making processes and disclosing potential limitations or biases14.

• Accountability: Establishing clear lines of responsibility for AI systems and their outcomes is essential. This ensures that individuals and organizations are held accountable for the decisions made by AI systems and any unintended consequences17.

• Privacy: Protecting the privacy of individuals and their data when using Generative AI is paramount. This involves implementing strong data protection measures, obtaining informed consent for data usage, and ensuring compliance with privacy regulations14.

6. Responsible AI Development Practices

Familiarize yourself with responsible AI development practices:

• Diverse and representative datasets: Using training data that reflects the diversity of the population helps avoid bias and ensures that AI systems are fair and inclusive. This requires actively seeking out data that represents different demographics, cultures, and perspectives18.

• Bias detection and mitigation: Implementing techniques to identify and mitigate biases in AI models is crucial. This involves using tools and methods to detect bias in training data and algorithms, and employing strategies to reduce or eliminate these biases18.

• Transparency and explainability: Developing AI models that are transparent and explainable makes it easier to understand their decision-making processes. This promotes trust and allows for better oversight of AI systems21.

• Continuous monitoring and evaluation: Regularly monitoring and evaluating AI systems ensures they are performing as intended and addresses any emerging issues. This involves tracking AI performance over time, identifying potential problems, and making necessary adjustments to maintain safety and responsibility19.

Online Courses and Workshops

In addition to research papers, blogs, and articles, online courses and workshops can provide valuable structured learning opportunities and practical experience in Generative AI safety. Here are some recommended resources:

• Generative AI Made Easy: Start Your Generative AI Journey 24: This Udemy course provides a beginner-friendly introduction to Generative AI, covering fundamental concepts, tools, and techniques.

• SEC545: GenAI and LLM Application Security 25: This SANS Institute course focuses on the security risks associated with Generative AI applications and provides hands-on training in implementing security controls.

• Navigating Generative AI Risks for Leaders 26: This Coursera course explores the risks and concerns associated with Generative AI, with a focus on ethical considerations and responsible AI development.

• Generative AI for Cybersecurity 27: This EC-Council course explores the use of Generative AI in cybersecurity, covering both defensive and offensive applications.

• AI Safety 28: This Learn Prompting course provides a comprehensive introduction to AI safety, covering ethical, legal, and security considerations.

• Safe Generative AI Workshop 29: This workshop brings together experts to discuss AI safety concerns related to generative models, exploring potential solutions and fostering collaboration.

• Generative AI Cybersecurity Awareness Training 30: This Johnson County Community College workshop focuses on detecting, preventing, and responding to AI-driven threats, providing practical strategies for cybersecurity professionals.

Creating Your Study Plan

This learning path provides a framework for learning about Generative AI safety. You can tailor it to your specific needs and interests. Here’s a suggested approach:

1. Start with the basics: Begin by reading introductory articles and blog posts to gain a general understanding of Generative AI and its safety implications.

2. Dive into research papers: Explore the foundational research papers listed in this guide to deepen your knowledge of key concepts and challenges.

3. Focus on specific areas: Choose one or two areas of risk that are particularly interesting or relevant to you and delve deeper into those topics.

4. Explore mitigation techniques: Learn about the various techniques and methods used to mitigate safety risks in Generative AI.

5. Consider ethical implications: Reflect on the ethical considerations surrounding Generative AI development and deployment.

6. Stay updated: The field of Generative AI safety is constantly evolving. Stay updated on the latest research, best practices, and emerging challenges.

Conclusion

Generative AI holds immense potential, but it’s crucial to approach its development and deployment with a focus on safety and responsibility. This learning path provides a roadmap for acquiring the knowledge and skills needed to navigate the complex landscape of Generative AI safety. By engaging with research papers, blogs, articles, online courses, and workshops, you can gain a comprehensive understanding of the challenges and opportunities in this rapidly evolving field. A holistic approach to Generative AI safety requires considering technical, ethical, and societal aspects, ensuring that these powerful technologies are used to benefit humanity while mitigating potential risks.

Works cited

1. 7 Generative AI Security Risks & How to Defend Your Organization, accessed on March 8, 2025, https://www.tigera.io/learn/guides/llm-security/generative-ai-security-risks/

2. Generative AI Security Risks: Mitigation & Best Practices - SentinelOne, accessed on March 8, 2025, https://www.sentinelone.com/cybersecurity-101/data-and-ai/generative-ai-security-risks/

3. Security Risks of Generative AI and Countermeasures, and Its …, accessed on March 8, 2025, https://www.nttdata.com/global/en/insights/focus/2024/security-risks-of-generative-ai-and-countermeasures

4. The flip side of generative AI - KPMG International, accessed on March 8, 2025, https://kpmg.com/us/en/articles/2023/generative-artificial-intelligence-challenges.html

5. The Top Five Risks of Generative AI & How to Mitigate Them …, accessed on March 8, 2025, https://ceriumnetworks.com/the-top-five-risks-of-generative-ai-amp-how-to-mitigate-them/

6. ojs.aaai.org, accessed on March 8, 2025, https://ojs.aaai.org/index.php/AIES/article/download/31717/33884/35781

7. AI safety – ETO Research Almanac, accessed on March 8, 2025, https://almanac.eto.tech/topics/ai-safety/

8. Computational Safety for Generative AI: A Signal Processing …, accessed on March 8, 2025, https://www.arxiv.org/abs/2502.12445

9. Generative AI Security Risks: 8 Key Threats to Know - Keepnet, accessed on March 8, 2025, https://keepnetlabs.com/blog/generative-ai-security-risks-8-critical-threats-you-should-know

10. Oops! 5 serious gen AI security mistakes to avoid | Google Cloud Blog, accessed on March 8, 2025, https://cloud.google.com/transform/oops-5-serious-gen-ai-security-mistakes-to-avoid

11. Disrupting a global cybercrime network abusing generative AI …, accessed on March 8, 2025, https://blogs.microsoft.com/on-the-issues/2025/02/27/disrupting-cybercrime-abusing-gen-ai/

12. What are GenAI cybersecurity threats? - Serokell, accessed on March 8, 2025, https://serokell.io/blog/security-risks-of-generative-ai

13. Best Strategies to Reduce Generative AI Risk - Centraleyes, accessed on March 8, 2025, https://www.centraleyes.com/generative-ai-risk/

14. Ethics and Costs - Generative AI - Research Guides at Amherst College, accessed on March 8, 2025, https://libguides.amherst.edu/c.php?g=1350530&p=9969379

15. Ethical Concerns about Generative AI - A.I. (Artificial Intelligence) & Information Literacy - Wolfgram Subject Guides at Widener University, accessed on March 8, 2025, https://widener.libguides.com/c.php?g=1364905&p=10144832

16. Using Generative AI: Ethical Considerations - University of Alberta Library Subject Guides, accessed on March 8, 2025, https://guides.library.ualberta.ca/generative-ai/ethics

17. Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective - MDPI, accessed on March 8, 2025, https://www.mdpi.com/2227-9709/11/3/58

18. How to implement responsible AI practices | SAP, accessed on March 8, 2025, https://www.sap.com/resources/what-is-responsible-ai

19. 7 actions that enforce responsible AI practices - Huron, accessed on March 8, 2025, https://www.huronconsultinggroup.com/insights/seven-actions-enforce-ai-practices

20. Responsible AI: Key Principles and Best Practices | Atlassian, accessed on March 8, 2025, https://www.atlassian.com/blog/artificial-intelligence/responsible-ai

21. What is responsible AI? | IBM, accessed on March 8, 2025, https://www.ibm.com/think/topics/responsible-ai#:~:text=Integrate%20ethics%20across%20the%20AI%20development%20lifecycle&text=Regularly%20assess%20models%20for%20fairness,%2C%20algorithms%2C%20and%20decision%20processes.

22. Embracing the Future: A Comprehensive Guide to Responsible AI …, accessed on March 8, 2025, https://www.lakera.ai/blog/responsible-ai

23. 7 Development Principles of AI systems - Chirpn, accessed on March 8, 2025, https://chirpn.com/insight-details/guide-to-responsible-development-of-ai-systems/

24. Top Generative AI (GenAI) Courses Online - Updated [March 2025] - Udemy, accessed on March 8, 2025, https://www.udemy.com/topic/generative-ai/

25. SEC545: GenAI and LLM Application Security™ | SANS Institute, accessed on March 8, 2025, https://www.sans.org/cyber-security-courses/genai-llm-application-security/

26. Navigating Generative AI Risks for Leaders - Coursera, accessed on March 8, 2025, https://www.coursera.org/learn/navigating-generative-ai-risks-for-leaders

27. Generative AI for Cybersecurity | EC-Council Learning, accessed on March 8, 2025, https://codered.eccouncil.org/course/generative-ai-for-cybersecurity-course

28. AI Safety - Courses - Learn Prompting, accessed on March 8, 2025, https://learnprompting.org/courses/ai-safety

29. Safe Generative AI Workshop, accessed on March 8, 2025, https://safegenaiworkshop.github.io/

30. Generative AI Cybersecurity Awareness Training - Detect, Prevent & Respond to AI-Driven Threats, accessed on March 8, 2025, https://continuinged.jccc.edu/courses/detail/46341