DOGE Used AI for Housing Policy. The Government Won't Say How In response to a public records request, HUD has withheld documents about DOGE's use of AI--in part by citing a privilege that doesn't exist. Members of the so-called Department of Government Efficiency (DOGE) who were working at the Department of Housing and Urban Development (HUD) used artificial intelligence to inform policy decisions. Now, the agency appears to be denying Freedom of Information Act requests for information on the development and use of AI tools, and the way they informed policy decisions, according to documents obtained by a FOIA request by Democracy Forward, a nonprofit legal organization. Last year, WIRED reported that Christopher Sweet, who was then a third-year student at the University of Chicago, had joined the DOGE team at HUD, along with Scott Langmack, who came to DOGE from a property technology startup called Kukun. Sweet's primary focus, according to HUD employees who spoke to WIRED at the time, was on using artificial intelligence to identify agency rules for potential rescission, or contract cancellations, as part of a similar effort across the government .

Assessing the quality of group deliberation is essential for improving our understanding of deliberative processes. The Deliberative Reason Index (DRI) offers a sophisticated metric for evaluating group reasoning, but its implementation has been constrained by the complex and time-consuming process of statement generation. This thesis introduces an innovative, automated approach to DRI statement generation that leverages advanced natural language processing (NLP) and large language models (LLMs) to substantially reduce the human effort involved in survey preparation. Key contributions are a systematic framework for automated DRI statement generation and a methodological innovation that significantly lowers the barrier to conducting comprehensive deliberative process assessments. In addition, the findings provide a replicable template for integrating generative artificial intelligence into social science research methodologies.

The conceptual framework proposed in this paper centers on the development of a deliberative moral reasoning system - one designed to process complex moral situations by generating, filtering, and weighing normative arguments drawn from diverse ethical perspectives. While the framework is rooted in Machine Ethics, it also makes a substantive contribution to Value Alignment by outlining a system architecture that links structured moral reasoning to action under time constraints. Grounded in normative moral pluralism, this system is not constructed to imitate behavior but is built on reason-sensitive deliberation over structured moral content in a transparent and principled manner. Beyond its role as a deliberative system, it also serves as the conceptual foundation for a novel two-level architecture: functioning as a moral reasoning teacher envisioned to train faster models that support real-time responsiveness without reproducing the full structure of deliberative reasoning. Together, the deliberative and intuitive components are designed to enable both deep reflection and responsive action. A key design feature is the dual-hybrid structure: a universal layer that defines a moral threshold through top-down and bottom-up learning, and a local layer that learns to weigh competing considerations in context while integrating culturally specific normative content, so long as it remains within the universal threshold. By extending the notion of moral complexity to include not only conflicting beliefs but also multifactorial dilemmas, multiple stakeholders, and the integration of non-moral considerations, the framework aims to support morally grounded decision-making in realistic, high-stakes contexts.

Rep. Ken Buck, R-Colo., spoke with Fox News Digital about his bill to establish a commission to address concerns about AI's rapid development. A GOP lawmaker leading on Congress' response to Big Tech is calling for a commission to streamline the U.S.'s development of artificial intelligence technology, warning that Congress is moving "too slow" on the rapidly advancing sector. Rep. Ken Buck, R-Colo., teamed up with Democratic Reps. Ted Lieu and Anna Eshoo this week to introduce the National AI Commission Act, which calls for a panel of 20 experts across various facets of AI to convene and advise the U.S. government on the risks and opportunities associated with it. "I think that we should look at this bill very closely and move it very quickly," Buck told Fox News Digital.

Technologist and researcher Aviv Ovadya isn't sure that generative AI can be governed, but he thinks the most plausible means of keeping it in check might just be entrusting those who will be impacted by AI to collectively decide on the ways to curb it. That means you; it means me. It's the power of large networks of individuals to problem solve faster and more equitably than a small group of individuals might do alone (including, say, in Washington). In Taiwan, for example, civic-minded hackers in 2015 formed a platform -- "virtual Taiwan" -- that "brings together representatives from the public, private and social sectors to debate policy solutions to problems primarily related to the digital economy," as explained in 2019 by Taiwan's digital minister, Audrey Tang in the New York Times. Since then, vTaiwan, as it's known, has tackled dozens of issues by "relying on a mix of online debate and face-to-face discussions with stakeholders," Tang wrote at the time.