Cross-note synthesis

Range and diversity of participants

How the published AI CoLab notes, taken together, speak to the Range and diversity of participants evaluation theme. Public, general synthesis — safe to draw on without checking. It generalises the lessons; the sources below link back to the particular workshops (some members-gated).

25 insight notes so far. Not every workshop becomes one — recurring sessions and smaller gatherings usually don't. For some of the larger workshops a transcript is turned into an insight note so its lessons can be shared — with the room's consent, through time, and with people who weren't there.

The diversity that matters tracks the question being asked. The notes do not support "more diversity is always better" so much as match the mix to what you need made visible. Surfacing structural barriers took intersectional lived experience — gender, ethnicity, class, neurodivergence (Unlocking career potential). Stress-testing a working method took a cross-sector span of government tiers, academia and widely varying AI experience (AI for Insight Notes). Making adoption friction visible took a range from confident hands-on practitioners to people new to both AI and government (Growing with AI). Pressure-testing modernisation techniques took a cross-disciplinary mix — coders, designers and non-technical participants in parallel streams (AI for the Systems You Inherited). Rehearsing a high-stakes public failure took people carrying direct professional experience of a real welfare-automation collapse, set beside an international vantage on how other governments approach the same problem (When the Algorithm Goes Rogue). And working a shared ethical dilemma took the broadest span in the corpus — federal, state and local government, health services, cultural institutions, university research, private-sector practice, and human-research-ethics and First Nations perspectives — with groups deliberately mixed to maximise difference (Beyond the Principles). Scoping a shared data capability drew custodians, destination managers, AI startups and analytics consultancies across tourism, finance and agriculture portfolios (Tourism data); examining what AI change demands of a profession drew senior people-and-culture leaders from across the public service, private firms and academia (People and Culture Leadership). Demystifying the statistical foundations of AI, in turn, drew a deliberately broad span — federal agencies, consultants, startups and industry engineers, academia, postgraduate students and independents — in which a show of hands found social-science backgrounds in the majority with substantial engineering overlap, the very cross-disciplinary mix the session argued made deep learning possible in the first place (Beyond the Black Box). Scoping authoritative-data infrastructure for AI agents took a smaller but deliberately cross-sector room — private-sector legal-technology practitioners alongside public-sector regulatory, compliance and policy roles — which kept an abstract protocol anchored to concrete provenance obligations and civic, public-good uses (Authoritative by Source).

Other sessions in the corpus stretch the range in directions the APS-centred ones do not. The startup-and-SME session convened a deliberately non-government room — founders, SME leaders and not-for-profits alongside public servants — so commercial and social-sector stakes, not only policy ones, shaped what "responsible use" had to mean (AI Ready?). The human-AI teaming session made disciplinary spread the method itself, drawing anthropology, mathematics, physics, law and economics into one exercise on the premise that the cross-disciplinary mix was what produced the result (Human-AI teaming). And the environmental-stewardship session points to the vantage the corpus most often lacks — traditional land-management and cultural-knowledge holders set beside environmental scientists and AI technologists, where the explicit question was how to embed cultural knowledge in a technical workflow (AI for environmental stewardship).

Outside vantage points earn their place. A regulator from outside the sector sharpened the agriculture discussion (Growing with AI), and private-sector practice set beside public-sector delivery did the same in both 2026 notes (AI for the Systems You Inherited); an international facilitator gave a welfare-systems room a comparative window onto how several other governments are handling the same problem (When the Algorithm Goes Rogue). The outsider's questions are part of what makes the harder issues visible.

A gap that is starting to close: tracing what the mix changed, not just who was in the room. Mostly the effect of diversity is asserted rather than traced, but two notes show how to do better. Growing with AI shows newcomers grounding the discussion in real adoption friction; Beyond the Principles goes further and treats the mix as a method — one shared scenario drew out distinct health, recruitment, community-consultation and cultural-heritage readings because of who was present, and the divergence was the finding. People and Culture Leadership does the same with generational range, where the spread of personal stances on AI directly shaped the conversation about meeting different cohorts where they are. And naming who was missing is part of the same discipline: Tourism data flags that the room was mainly government and analytics-side, with private tourism operators absent — a gap that bounds what the session could conclude, and Practical AI for Policy People does the same from an introductory session, recording an APS policy audience across several agencies but noting broader cross-sector representation was thinner. Future notes should follow all three: name the insight a given vantage point produced, and the gap a missing one left.

Sources

Last compiled 2026-06-23 from 25 published note(s).

Notes contributing to this theme: