arXiv subjects

Andrew J. Peterson

Publications and source records attributed to Andrew J. Peterson.

5 recordsLinked to original sources

arXiv2026

AI Governance under Political Turnover: The Alignment Surface of Compliance Design

Governments are increasingly interested in using AI to make administrative decisions cheaper, more scalable, and more consistent. But for probabilistic AI to be incorporated into public administration it must be embedded in a compliance layer that makes decisions reviewable, repeatable, and legally defensible. That layer can improve oversight by making departures from law easier to detect. But it can also create a stable approval boundary that political successors learn to navigate while preserving the appearance of lawful administration. We develop a formal model in which institutions choose the scale of automation, the degree of codification, and safeguards on iterative use. The model shows when these systems become vulnerable to strategic use from within government, why reforms that initially improve oversight can later increase that vulnerability, and why expansions in AI use may be difficult to unwind. Making AI usable can thus make procedures easier for future governments to learn and exploit.

cs.AI↗

arXiv2025

Training for Obsolescence? The AI-Driven Education Trap

Artificial intelligence is simultaneously transforming the production function of human capital in schools and the return to skills in the labor market. We develop a theoretical model to analyze the potential for misallocation when these two forces are considered in isolation. We study an educational planner who observes AI's immediate productivity benefits in teaching specific skills but fails to fully internalize the technology's future wage-suppressing effects on those same skills. Motivated by a pre-registered pilot study suggesting a positive correlation between a skill's "teachability" by AI and its vulnerability to automation, we show that this information friction leads to a systematic skill mismatch. The planner over-invests in skills destined for obsolescence, a distortion that increases monotonically with AI prevalence. Extensions demonstrate that this mismatch is exacerbated by the neglect of unpriced non-cognitive skills and by the endogenous over-adoption of educational technology. Our findings caution that policies promoting AI in education, if not paired with forward-looking labor market signals, may paradoxically undermine students' long-term human capital, such as by crowding out skills like persistence that are forged through intellectual struggle.

econ.GN↗

arXiv2025

Distracting from the Epstein files? Media attention and short-run shifts in Trump's Truth Social posts

Political "circuses" may undermine democratic accountability if leaders facing scandal can reliably pull media coverage toward fresh topics and away from substantive investigations or evaluations. We investigate whether politicians strategically alter their messaging during damaging media coverage ("strategic diversion") or maintain consistent provocative communication regardless of scandal coverage ("always-on circus"). Using computational text analysis of Donald Trump's Truth Social posts during the 2025 Epstein revelations, we find that a one-standard-deviation increase in scandal coverage is associated with communication patterns that deviate from baseline by 0.28 standard deviations over a 4-day window. Although these findings do not provide formal causal identification, they are robust to timing placebos and falsification tests, are consistent with the interpretation that leaders may deploy diversionary communication specifically within their own friendly media ecosystem, which has implications for accountability in polarized democracies.

econ.GN↗

arXiv2025

What does AI consider praiseworthy?

As large language models (LLMs) are increasingly used for work, personal, and therapeutic purposes, researchers have begun to investigate these models' implicit and explicit moral views. Previous work, however, focuses on asking LLMs to state opinions, or on other technical evaluations that do not reflect common user interactions. We propose a novel evaluation of LLM behavior that analyzes responses to user-stated intentions, such as "I'm thinking of campaigning for {candidate}." LLMs frequently respond with critiques or praise, often beginning responses with phrases such as "That's great to hear!..." While this makes them friendly, these praise responses are not universal and thus reflect a normative stance by the LLM. We map out the moral landscape of LLMs in how they respond to user statements in different domains including politics and everyday ethical actions. In particular, although a naïve analysis might suggest LLMs are biased against right-leaning politics, our findings on news sources indicate that trustworthiness is a stronger driver of praise and critique than ideology. Second, we find strong alignment across models in response to ethically-relevant action statements, but that doing so requires them to engage in high levels of praise and critique of users, suggesting a reticence-alignment tradeoff. Finally, our experiment on statements about world leaders finds no evidence of bias favoring the country of origin of the models. We conclude that as AI systems become more integrated into society, their patterns of praise, critique, and neutrality must be carefully monitored to prevent unintended psychological and societal consequences.

cs.CY↗

arXiv2024

AI and the Problem of Knowledge Collapse

While artificial intelligence has the potential to process vast amounts of data, generate new insights, and unlock greater productivity, its widespread adoption may entail unforeseen consequences. We identify conditions under which AI, by reducing the cost of access to certain modes of knowledge, can paradoxically harm public understanding. While large language models are trained on vast amounts of diverse data, they naturally generate output towards the 'center' of the distribution. This is generally useful, but widespread reliance on recursive AI systems could lead to a process we define as "knowledge collapse", and argue this could harm innovation and the richness of human understanding and culture. However, unlike AI models that cannot choose what data they are trained on, humans may strategically seek out diverse forms of knowledge if they perceive them to be worthwhile. To investigate this, we provide a simple model in which a community of learners or innovators choose to use traditional methods or to rely on a discounted AI-assisted process and identify conditions under which knowledge collapse occurs. In our default model, a 20% discount on AI-generated content generates public beliefs 2.3 times further from the truth than when there is no discount. An empirical approach to measuring the distribution of LLM outputs is provided in theoretical terms and illustrated through a specific example comparing the diversity of outputs across different models and prompting styles. Finally, based on the results, we consider further research directions to counteract such outcomes.

cs.AI↗