Pie & AI: Sydney - Unpacking Reponsible AI, Ethics, Laws and Regulations

Join us for the inaugural Pie & AI DeepLearning.AI Sydney community event were we dive into the world of responsible AI, AI ethics and AI regulations.

Theme: Understand current ethical issues working with AI as well as regulations in Australia and rest of the world.

With the every growing demand for AI's good there is also a challenge to keep up with and understand the various ethical, compliance, governance and risk issues. Hear from hands on practitioners that are working with ethical issues as well responsible AI and legal frameworks in Australia and beyond. Understand how ethics and regulation will play a part in how you shape your AI systems and projects in a very real and practical manner.

Agenda

• 6:00pm-6:30pm Networking
• 6:30pm-7:15pm Talk 1
• 7:15pm-8:00pm Talk 2
• 8:00pm Close
• Drinks at Cafe 80 next door for those that want to stay back to talk

Talks & Speakers

AI Regulation Landscape: Navigating the AI Regulations
Raymond Sun (Technology Lawyer, Herbert Smith Freehills) - Raymon is a practicing technology lawyer and developer with a focus on emerging technology law that has developed an AI regulation tracker and global tech law news hub.

• Raymon will walk us through current issues and how to navigate AI law in Australia as anyone working with AI products from procurement to privacy policies. Raymon will also give us a view of emerging global AI regulation in EU and US for those with global customers.

Responsible AI Engineering: Bridging the Gap between Policy and Practice.
Dr Qinghua Lu (Responsible AI Science Team Lead at CSIRO's Data61) - Dr Qinghua Lu is a principal research scientist and leads the Responsible AI science team at CSIRO's Data61. She is the winner of the 2023 APAC Women in AI Trailblazer Award and is part of the OECD.AI’s trustworthy AI metrics project team. She has published 150+ papers in premier international journals and conferences.

• The rapid advancements in AI, particularly with the emergence of large language models (LLMs) and their diverse applications, have attracted huge global interest and raised significant concerns on responsible AI and AI safety. While LLMs are impressive examples of AI models, it is the compound AI systems, which integrate these models with other key components for functionality and quality/risk control, that are ultimately deployed and have real-world impact.

• These AI systems, especially autonomous LLM agents and those involving multi-agent interacting, require system-level engineering to ensure responsible AI and AI safety. In this talk, Dr Lu will introduce a responsible AI engineering approach to address system-level responsible AI challenges. This includes engineering/governance methods, practices, tools, and platforms to ensure responsible AI and AI safety.

Thanks
Vincent Koc
DeepLearning.AI Ambassador