arXiv subjects

Angelica Chowdhury

Publications and source records attributed to Angelica Chowdhury.

4 recordsLinked to original sources

arXiv2026

Mapping U.S. Federal AI Governance Against Sector Vulnerability

Artificial intelligence (AI) poses different levels of risk across sectors, but are these differences reflected in U.S. federal AI governance? To help answer this question, we assess 684 federal AI governance documents for their coverage of 14 sectors and 24 AI risks. We measure coverage as breadth (i.e., how frequently the risk or sector is addressed across documents) and depth (i.e., how substantively the risk or sector is discussed). We then compare sector coverage patterns for each of the 24 risks with vulnerability assessments from a Delphi study of 272 experts. Our analysis finds substantial variation in coverage: AI risks related to robustness, system security, and governance receive more attention than socioeconomic, environmental, and emerging risks, including multi-agent risks. Public administration, national security, information, and scientific services receive comparatively high levels of coverage relative to other sectors, such as finance and healthcare, which experts rate as highly vulnerable to AI risks. By mapping current coverage and identifying where it differs from expert assessments of vulnerability, we surface potential AI governance gaps which may help inform AI risk-related decisions across government and industry.

cs.CY↗

arXiv2026

Principles and Guidelines for Randomized Controlled Trials in AI Evaluation

This work establishes a framework for standardizing AI evaluation RCTs (sometimes called human uplift studies). Drawing on established practices from disciplines with established RCT traditions, including software engineering, economics, clinical and health sciences, and psychology, we synthesize five principles drawn from established validity frameworks and open-science standards on transparency, repeatability, and verification, which together serve as the conceptual foundation for 33 actionable guidelines adapted for AI evaluation RCT contexts, expressed as requirements with rationales, implementation instructions, and evidence bases. We position the principles and guidelines as serving three key roles for AI evaluation RCTs: a design tool for planning studies, an evaluation rubric for assessing existing work, and a blueprint for standard setting as the field converges on norms. AI evaluation research currently lacks common standards and shared vocabulary for producing cumulative, comparable, policy-ready evidence. This framework is a contribution toward that foundation, providing evaluative criteria and a shared conceptual language alongside actionable guidelines.

cs.CY↗

arXiv2026

Incoherent Values? Probing LLM Preferences Through Parametric Variation

To trust another autonomous entity -- human or AI -- it helps to know that how it acts given one set of reasons is at least somewhat predictive of how it would act under others. It is hard to trust someone with incoherent values. Some think of Large Language Models as merely stochastic text generators with no evaluative core -- superpositions of billions of possible characters, not one stable identity. But others have argued that LLMs *do* have stable, emergent values, which can be elicited by presenting them with a series of forced choices between arbitrary statements, and which emerge as a function of model scale. In this paper, we test this thesis by presenting LLMs with parametric variations on those forced choices. We reason that if a model genuinely prefers A to B, then except in unusual circumstances it should also reject B in favor of an augmented version of A, which has more of what makes A desirable -- A++. Our results indicate that earlier attributions of coherence may have overstated their case. Even the most capable models exhibit significant incoherence, and coherence does not appear to emerge as a result of underlying model capability. We do, however, find that models given time to reason are less incoherent than those with thinking disabled. More generally, we develop a novel framework for eliciting and evaluating coherent values, which can be used both to assess how trustworthy current models are, and -- in future work -- to provide reward signal that can be used for making more coherent agents.

cs.CY↗

arXiv2025

Scaling Homomorphic Applications in Deployment

In this endeavor, a proof-of-concept homomorphic application is developed to determine the production readiness of encryption ecosystems. A movie recommendation app is implemented for this purpose and productionized through containerization and orchestration. By tuning deployment configurations, the computational limitations of Fully Homomorphic Encryption (FHE) are mitigated through additional infrastructure optimizations Index Terms: Reinforcement Learning, Orchestration, Homomorphic Encryption

cs.CR↗