The field of Natural Language Generation (NLG) and Generative AI is currently navigating through a complex landscape of ethical, safety, and security challenges, alongside its rapid technological advancements. A significant focus is on addressing dual-use issues, where the same technology can be used for both beneficial and harmful purposes. This concern has prompted the SIGGEN community to conduct surveys and discussions to better understand and mitigate these risks. Additionally, the practice of AI red teaming has emerged as a critical method for probing the safety and security of generative AI systems, offering insights into practical recommendations for aligning red teaming efforts with real-world risks. The development of comprehensive frameworks like SAIF for evaluating the risks of generative AI in the public sector and the ETHICAL framework for responsible GenAI use in research highlights the field's commitment to fostering safe, responsible, and ethical integration of generative AI technologies. These frameworks aim to provide systematic approaches to risk assessment and ethical considerations, ensuring that the transformative potential of generative AI is harnessed responsibly.

Noteworthy Papers

• Dual use issues in the field of Natural Language Generation: A survey within the SIGGEN community sheds light on dual-use issues, offering a foundation for future discussions on ethical NLG applications.
• Lessons From Red Teaming 100 Generative AI Products: Presents eight key lessons from red teaming operations, emphasizing the importance of understanding system capabilities and the human element in AI security.
• SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sector: Introduces a systematic framework for assessing generative AI risks in public sector applications, ensuring a comprehensive evaluation approach.
• Navigating Ethical Challenges in Generative AI-Enhanced Research: The ETHICAL Framework for Responsible Generative AI Use: Develops a practical guide for ethical GenAI use in research, addressing the gap between awareness of ethical issues and actionable steps.