{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"ADAPT Insider","title":"How the Australian government uses AI to solve complex public problems safely and at scale","html":"","width":"100%","height":180,"duration":949,"description":"What does safe scale look like when the cost of getting AI wrong is measured in public trust?\nIn this ADAPT Insider podcast episode, Daniela Polit, Public Sector Transformation Executive, outlines a clear test for government AI. It should help solve complex public problems, reduce friction for citizens, and improve services at scale, while operating within guardrails that protect sovereignty, accountability, and trust.\n\nKey takeaways:\nAI should only be used when it clearly improves a public outcome, whether that means faster service, less friction, better inclusivity, or more efficient processing.\nTrust depends on keeping sovereignty, transparency, and human accountability intact, with AI used inside closed environments and final decisions always staying with people.\nSafer AI adoption comes from matching governance, training, and oversight to the level of public risk, rather than applying the same approach everywhere.\n\nPublic value has to come before the technology\n\nStrong AI strategies begin with the problem being solved.\nIn government, that means asking whether a tool can genuinely improve a service, shorten wait times, reduce bureaucracy, or make support easier to access for citizens.\nThat is the lens Daniela applies throughout the conversation.\nShe describes AI as a way to solve complex public sector problems, especially where service delivery involves scale, complexity, and large volumes of information.\nThe value, in her view, comes from helping people deal with government faster and with less friction, whether that means reducing unnecessary touchpoints, improving transparency, or tailoring services more effectively across very different citizen needs.\nIf AI can clearly improve the outcome, it has a case. If the likely value is marginal and the risk is higher, it should not be forced in.\n\nTrust holds when sovereignty and accountability are protected\n\nPublic sector AI needs trust built into where models run, how data is handled, and who remains responsible...","thumbnail_url":"https://img.transistorcdn.com/t0Jtrt9eZN4kwrPnSzLt-aLKB9f-tVVg1JGt9pql9t4/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS84ODM3/NGVhYmU2ZjljNWIx/NTliY2NlMTAzZDRl/NGNmOS5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}