Matthew Moore

This week, Privacy Commissioner Carly Kind determined that Kmart's use of facial recognition tech breached Australia's privacy law. This was an important moment. Kmart argued that it needed to act robustly to address the problems of theft and refund fraud in its stores. The Privacy Commissioner accepted this, but she also made clear that how companies like Kmart combat these problems truly matters. The core issues in this case are straightforward. First, Kmart's facial recognition system created something like a virtual 'police line up'. Tens of thousands of ordinary Australians, walking through the stores that used this tech, were effectively treated as suspects, even when they'd done nothing wrong. Second, facial recognition didn't 'add up' as a way of addressing Kmart's problem. There were basic things Kmart could do - like requiring customers who wanted refunds to bring a receipt for the products they claimed to have bought - and these wouldn't impact nearly as much on Australians' basic right to privacy. In other words, before adopting a radical measure like facial recognition to address things like shop lifting, companies like Kmart should first do the more obvious measures that wouldn't have a disproportionate impact on people's right to privacy. This case is a reminder that the Australian Government committed some years ago to update our Privacy Act to make crystal clear when facial recognition can and can't be used. This is more urgent than ever. I was pleased to speak with Tom Stayner of SBS for this story (along with Lizzie O'Shea FAAL from Digital Rights Watch). https://lnkd.in/gT4vPpyW

133

8 Comments

Deloitte has been caught blatantly using AI to generate reports for commissioned consulting projects and will now repay the Albanese government after using AI in a $440,000 report, a fact uncovered by a professor on the government committee.It’s not about one firm or one government. Governments across the world should be cautious about blindly trusting consultants who are often not subject-matter experts, but rather glorified salespeople.

60

12 Comments

Trust, Attitudes and Use of Artificial Intelligence | prepared by KPMG in collaboration with The University of Melbourne This report examines the evolving landscape of #AI adoption, #publictrust, and organizational deployment strategies, highlighting the nuanced interplay between technological capability, regulatory frameworks, and societal perception. Drawing on survey data from over 5,400 participants across #Australia, #theUnitedKingdom, and #theUnitedStates, and interviews with 87 senior executives, the study situates AI not merely as a tool but as a socio-technical system where trust, governance, and ethical alignment directly impact adoption rates and value creation. The research frames AI deployment within three interdependent dimensions: organizational readiness, user perception, and accountability mechanisms, revealing systemic disparities across sectors and geographies that influence both uptake and efficacy. Quantitative findings underscore the critical role of trust in AI adoption: 64% of respondents expressed cautious optimism about AI’s benefits, yet only 38% indicated strong confidence in existing oversight mechanisms. In organizations actively deploying AI, 71% reported integrating ethical review boards or AI governance committees, yet only 29% had formalized explainability protocols for end-users. Cross-sector analysis highlights that highly regulated industries such as finance and healthcare demonstrate higher trust scores—averaging 58%—relative to #consumertech and #retail, where confidence falls to 41%. Moreover, AI literacy emerged as a significant predictor of acceptance: participants with moderate to high AI familiarity were 2.3 times more likely to endorse AI-assisted decision-making in critical operations, emphasizing the interaction between competence, transparency, and trust. The report introduces a conceptual framework of “Trust-Enabled AI,” integrating perceptions, governance structures, and adoption metrics into a single evaluative model. Pilot case studies illustrate that firms embedding continuous monitoring, algorithmic transparency, and stakeholder engagement in AI systems achieve adoption rates 22–27% higher than peers with minimal governance interventions. Additionally, public sentiment analysis reveals that narrative framing and proactive communication about AI limitations can improve perceived legitimacy, thereby mitigating reputational and #operationalrisks. In summary, AI adoption and efficacy are inextricably linked to the triad of trust, competence, and governance. Building resilient AI ecosystems requires organizations to implement robust oversight, enhance user literacy, and align algorithmic decision-making with societal values. The findings signal that without deliberate #investment in trust infrastructure, AI #risks engendering skepticism and uneven uptake, undermining its transformative potential across both public and private sectors.

41

1 Comment