Double-header day on my favourite topic โ ๐ฑ๐ฒ๐๐ถ๐ด๐ป๐ถ๐ป๐ด ๐ต๐๐บ๐ฎ๐ป ๐ผ๐๐ฒ๐ฟ๐๐ถ๐ด๐ต๐.
โข Remote presentation at the Symposium on Responsible AI in Public Administration (organised by the wonderful Jason Grant Allen, bringing together law and AI experts in the same room in Singapore)
โข Fireside chat with Rasika Mohan at the AI Governance Summit
Thereโs a joke in the AI community:ย ๐ฉ๐๐ ๐ข๐ค๐ข๐๐ฃ๐ฉ ๐ฎ๐ค๐ช ๐๐ช๐ก๐ก๐ฎ ๐ช๐ฃ๐๐๐ง๐จ๐ฉ๐๐ฃ๐ ๐๐ค๐ฌ ๐๐ฃ ๐ผ๐ ๐ฌ๐ค๐ง๐ ๐จ, ๐๐ฉโ๐จ ๐ฃ๐ค ๐ก๐ค๐ฃ๐๐๐ง ๐ผ๐ โ ๐๐ช๐จ๐ฉ ๐๐ค๐ง๐๐ฃ๐ ๐๐ช๐ฉ๐ค๐ข๐๐ฉ๐๐ค๐ฃ. If you can fully understand how an agent comes up with a plan or an answer, and you have complete control over how it operates, itโs no longer really an autonomous agent โ itโs automation.
๐๐ฒ๐ฟ๐ฒโ๐ ๐๐ต๐ฒ ๐ผ๐
๐๐บ๐ผ๐ฟ๐ผ๐ป ๐ถ๐ป ๐ด๐ผ๐๐ฒ๐ฟ๐ป๐ถ๐ป๐ด ๐ฎ๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐:
Governance demands transparency and control, while true agentic AI involves human giving up some understanding and control.
Many oversight mechanisms try, with mixed success, to force understanding and control back into an agent, effectively turning it into automation. That doesnโt work.
๐ง๐ต๐ฒ๐ป ๐๐ต๐ฒ ๐ต๐ฎ๐ฟ๐ฑ๐ฒ๐ฟ ๐ฐ๐ต๐ฎ๐น๐น๐ฒ๐ป๐ด๐ฒ:ย ๐๐ต๐ฎ๐ ๐ถ๐ณ ๐๐ผ๐๐ฟ ๐๐ ๐ฎ๐ด๐ฒ๐ป๐ ๐ถ๐ ๐๐บ๐ฎ๐ฟ๐๐ฒ๐ฟ ๐๐ต๐ฎ๐ป ๐๐ผ๐?
Many oversight mechanisms often assume the oversight role is to catch mistakes. But great leaders manage people far smarter than themselves every day. Oversight here is rarely about catching errors, but it can still be deeply meaningful, just in different ways.
Thatโs why this line of work at CSIRO’s Data61 excites me the most. The future of oversight is not about hoping the AI slips up so you still have a job. Itโs about adding value without being smarter than AI โ using tools, scaling yourself, growing your understanding, and shaping outcomes. Thatโs the real human productivity edge.
Stay tuned.

