Recent research in AI governance is increasingly focused on establishing frameworks that can keep pace with the rapid evolution of AI technologies. One significant area of exploration is the automation of AI research and development, which raises questions about the balance between capability advancement and safety oversight. Metrics have been proposed to better understand this dynamic, enabling stakeholders to track the implications of AI R&D automation. Concurrently, the introduction of the Sentience Readiness Index highlights the need for national preparedness in the face of potential AI sentience, revealing widespread inadequacies in institutional and cultural readiness. Additionally, the concept of Institutional AI is gaining traction, proposing governance structures that can mitigate risks associated with multi-agent AI systems. As AI continues to permeate various sectors, these developments underscore the urgency for robust legal and regulatory infrastructures that not only set rules but also adapt to the complexities of AI decision-making, ensuring that human oversight remains integral in an increasingly automated landscape.
We introduce the Dynamic Behavioral Constraint (DBC) benchmark, the first empirical framework for evaluating the efficacy of a structured, 150-control behavioral governance layer, the MDBC (Madan DBC)...
Present practice of deciding on regulation faces numerous problems that make adopted regulations static, unexplained, unduly influenced by powerful interest groups, and stained with a perception of il...
The automation of AI R&D (AIRDA) could have significant implications, but its extent and ultimate effects remain uncertain. We need empirical data to resolve these uncertainties, but existing data (pr...
The scientific study of consciousness has begun to generate testable predictions about artificial systems. A landmark collaborative assessment evaluated current AI architectures against six leading th...
This article sets off for an exploration of the still evolving discourse surrounding artificial intelligence (AI) in the wake of the release of ChatGPT. It scrutinizes the pervasive narratives that ar...
When organisations adopt commercial AI systems for decision support, they inherit value judgements embedded by vendors that are neither transparent nor renegotiable. The governance puzzle is not wheth...
The rapid adoption of large language models (LLMs) has enabled new forms of AI-assisted reasoning across scientific, technical, and organizational domains. However, prevailing modes of LLM use remain ...
Dataset documentation is widely recognized as essential for the responsible development of automated systems. Despite growing efforts to support documentation through different kinds of artifacts, lit...
Multi-agent LLM ensembles can converge on coordinated, socially harmful equilibria. This paper advances an experimental framework for evaluating Institutional AI, our system-level approach to AI align...
The rise of model hubs has made it easier to access reusable model components, making model merging a practical tool for combining capabilities. Yet, this modularity also creates a \emph{governance ga...