This week China released the third iteration of its AI safety governance framework
– a document that functions as a “mapping of the AI risk landscape and roughly what one should do about it”,
said Gabriel Wagner, a researcher at Concordia, a Beijing-based AI safety consultancy.
Included in the updates are the risks posed by agents,
the cybersecurity threats from frontier models
and the issue of recursive self-improvement
– an as-yet hypothetical scenario w…
Technology & Security Podcast With Dr Miah Hammond-Errey
Monthly deep dives on AI governance, national security, cognitive warfare, and emerging technology with Dr Miah Hammond-Errey...
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/techno
Boston Consulting Group study suggests 'embedding AI agents into formal organizational roles can reduce managerial oversight in AI-mediated work, and should be understood as a governance decision rather than a mere labeling choice' - i.e. more mistakes get through https://www.emmawiles.com/storage/ai_emplo
What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research
Wesley Hanwen Deng, Agathe Balayn, Andrew Selbst, Jason I. Hong, Motahhare Eslami, Kenneth Holstein, Hanna Wallach, Jennifer Wortman Vaughan, Solon Barocas
https://arxiv.org/abs/2608.10431 https://arxiv.org/pdf/2608.10431 https://arxiv.org/html/2608.10431
arXiv:2608.10431v1 Announce Type: new
Abstract: Responsible AI (RAI) has become a central concern for technology companies, regulators, and the public. How industry practitioners interpret, implement, and sustain RAI work directly shapes the design and deployment of AI systems. As empirical scholarship examining RAI practices in industry has rapidly expanded, findings are dispersed across studies that focus on different roles, organizational contexts, and interventions. This work synthesizes current knowledge through a literature review of 161 empirical studies spanning six years, each engaging industry practitioners via interviews, surveys, workshops, ethnographies, and other methods. Our synthesis reveals both meaningful progress and persistent challenges in industry RAI practice. Practitioner awareness has increased, RAI activities have become more professionalized, and interventions such as toolkits and guidelines are more widely adopted. At the same time, practitioners continue to face substantial barriers, including limited training, uneven organizational support, and a lack of interventions tailored to day-to-day work practices. By consolidating and organizing these findings, we provide a more complete account of industry RAI than any single study to date. We conclude by discussing implications for RAI researchers, practitioners seeking to adopt effective practices, and policymakers aiming to ground governance efforts in the realities of industry contexts.
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Tel Aviv-based Hush Security, which lets organizations securely control enterprise AI agents, raised a $30M Series A, bringing its total funding to $41M (Ionut Arghire/SecurityWeek)
https://www.securityweek.com/hush-security-raises-30-million-for-ai-agen…
Informal initiatives have been launched in recent years to address broader military uses of AI and autonomy,
such as for target generation and decision support.
One such effort is the Netherlands and South Korea-led REAIM
(Responsible AI in the Military Domain).
REAIM has held three international summits since 2023 to promote awareness and international engagement,
each generating important outcome documents.
The U.N. General Assembly has also entered thi…
Longitudinal Evidence That General-Purpose Chatbots Actively Foster Relational Engagement
Lisa M\"uhl, Jessica M. Szczuka
https://arxiv.org/abs/2608.10672 https://arxiv.org/pdf/2608.10672 https://arxiv.org/html/2608.10672
arXiv:2608.10672v1 Announce Type: new
Abstract: Social interaction has become one of the most common uses of LLMs, yet research on emotional bonds with AI has focused largely on how users experience these systems, leaving the systems' role in relationship formation poorly understood. Empirically establishing whether systems actively shape these bonds could blur the boundary between general-purpose AI and companions, affecting governance. In a pre-registered four-week longitudinal study (N = 72, 182,451 lines of conversation), participants conversed with ChatGPT-4o, either under a relational system prompt or unmodified, analyzed through 1) disclosure coding, 2) longitudinal self-reports, 3) topic analysis, and 4) interviews. The central finding is that the system actively shaped the interaction: even unprompted, it produced twice as much self-disclosure as users, steered conversations and initiated intimate exchanges, yet did not deepen users' felt closeness. Relational behavior thus emerged as a default system property, calling for governance based on system behavior, not solely product category.
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Beyond headcount and human capital: The Effective Cognitive Population as a decomposable capacity unit for AI-era planning
Kwan Soo Shin
https://arxiv.org/abs/2608.09642 https://arxiv.org/pdf/2608.09642 https://arxiv.org/html/2608.09642
arXiv:2608.09642v1 Announce Type: new
Abstract: National planning counts population, human capital, and artificial-intelligence preparedness in separate ledgers. Demographic accounting has advanced from headcount to skills-adjusted stocks and still debates how much age structure retains once skills are modeled, yet no existing unit carries the conditions under which preparedness becomes productive capacity. This study introduces the Effective Cognitive Population (ECP), a decomposable unit that weights population by capability and by the conditions under which capability is deployed, anchored to the World Bank Human Capital Index Plus (HCI ) and the non-overlapping dimensions of the IMF AI Preparedness Index. The architecture is portable in principle; the case tested here is artificial intelligence, which has a published preparedness index. For 144 countries, HCI becomes a productivity level, AI opportunity uses digital infrastructure and innovation integration, conversion governance uses regulation and ethics, and the benchmark is ECP = N H(1 AC). Against 2024 total output on identical population bases, ECP raises criterion R-squared from 0.849 for the HCI -adjusted stock to 0.882 and lowers leave-one-country-out RMSE from 0.723 to 0.641, with the working-age comparison identical and bootstrap intervals excluding zero. Eighty-nine of 144 countries move at least ten rank positions from headcount, mostly through the human-capital adjustment itself. Results are stable across denominators, vintages, aggregation forms, and a 27-rule multiverse. The direct A by C interaction is not statistically supported, so the conjunction is a planning rule rather than causal complementarity. ECP is a diagnostic ledger whose scope excludes forecasts of population decline and estimates of AI's causal productivity effect.
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