arXiv subjects

Brian D. Earp

Publications and source records attributed to Brian D. Earp.

8 recordsLinked to original sources

arXiv2026

AI and the Future of Academic Peer Review

Peer review remains the central quality-control mechanism of science, yet its ability to fulfill this role is increasingly strained. Empirical studies document serious shortcomings: long publication delays, escalating reviewer burden concentrated on a small minority of scholars, inconsistent quality and low inter-reviewer agreement, and systematic biases by gender, language, and institutional prestige. Decades of human-centered reforms have yielded only marginal improvements. Meanwhile, artificial intelligence, especially large language models (LLMs), is being piloted across the peer-review pipeline by journals, funders, and individual reviewers. Early studies suggest that AI assistance can produce reviews comparable in quality to humans, accelerate reviewer selection and feedback, and reduce certain biases, but also raise distinctive concerns about hallucination, confidentiality, gaming, novelty recognition, and loss of trust. In this paper, we map the aims and persistent failure modes of peer review to specific LLM applications and systematically analyze the objections they raise alongside safeguards that could make their use acceptable. Drawing on emerging evidence, we show that targeted, supervised LLM assistance can plausibly improve error detection, timeliness, and reviewer workload without displacing human judgment. We highlight advanced architectures, including fine-tuned, retrieval-augmented, and multi-agent systems, that may enable more reliable, auditable, and interdisciplinary review. We argue that ethical and practical considerations are not peripheral but constitutive: the legitimacy of AI-assisted peer review depends on governance choices as much as technical capacity. The path forward is neither uncritical adoption nor reflexive rejection, but carefully scoped pilots with explicit evaluation metrics, transparency, and accountability.

cs.CY↗

arXiv2026

Who Owns My AI Twin? Data Ownership in a New World of Simulated Identities

The emergence of AI twins, digital replicas that encapsulate an individual's knowledge, memories, psychological traits, and behavioral patterns, raises novel legal and ethical challenges for data governance and personal identity. Built from personal data, these systems require a rethinking of what it means to exercise dominion over one's data and to maintain personal autonomy in an AI-mediated environment. This article argues that natural persons should be recognized as the moral and legal owners of their AI twins, which function as intimate extensions of the self rather than as proprietary technological artifacts. It critiques prevailing legal frameworks that prioritize technological infrastructure and platform control over data and individual autonomy, exposing their structural limitations. In response, the article advances a human-centric model of data governance grounded in individual dominion and a private-by-default principle. This approach proposes a reimagined social contract for AI-driven identities that strengthens personal agency, promotes equitable data stewardship, and better aligns legal norms with the socio-technical realities of AI twins.

cs.HC↗

arXiv2026

A Quantifiable Information-Processing Hierarchy Provides a Necessary Condition for Detecting Agency

As intelligent systems are developed across diverse substrates - from machine learning models and neuromorphic hardware to in vitro neural cultures - understanding what gives a system agency has become increasingly important. Existing definitions, however, tend to rely on top-down descriptions that are difficult to quantify. We propose a bottom-up framework grounded in a system's information-processing order: the extent to which its transformation of input evolves over time. We identify three orders of information processing. Class I systems are reactive and memoryless, mapping inputs directly to outputs. Class II systems incorporate internal states that provide memory but follow fixed transformation rules. Class III systems are adaptive; their transformation rules themselves change as a function of prior activity. While not sufficient on their own, these dynamics represent necessary informational conditions for genuine agency. This hierarchy offers a measurable, substrate-independent way to identify the informational precursors of agency. We illustrate the framework with neurophysiological and computational examples, including thermostats and receptor-like memristors, and discuss its implications for the ethical and functional evaluation of systems that may exhibit agency.

q-bio.NC↗

arXiv2025

The Provenance Problem: LLMs and the Breakdown of Citation Norms

The increasing use of generative AI in scientific writing raises urgent questions about attribution and intellectual credit. When a researcher employs ChatGPT to draft a manuscript, the resulting text may echo ideas from sources the author has never encountered. If an AI system reproduces insights from, for example, an obscure 1975 paper without citation, does this constitute plagiarism? We argue that such cases exemplify the 'provenance problem': a systematic breakdown in the chain of scholarly credit. Unlike conventional plagiarism, this phenomenon does not involve intent to deceive (researchers may disclose AI use and act in good faith) yet still benefit from the uncredited intellectual contributions of others. This dynamic creates a novel category of attributional harm that current ethical and professional frameworks fail to address. As generative AI becomes embedded across disciplines, the risk that significant ideas will circulate without recognition threatens both the reputational economy of science and the demands of epistemic justice. This Perspective analyzes how AI challenges established norms of authorship, introduces conceptual tools for understanding the provenance problem, and proposes strategies to preserve integrity and fairness in scholarly communication.

cs.CY↗

arXiv2025

Authorship Without Writing: Large Language Models and the Senior Author Analogy

The use of large language models (LLMs) in bioethical, scientific, and medical writing remains controversial. While there is broad agreement in some circles that LLMs cannot count as authors, there is no consensus about whether and how humans using LLMs can count as authors. In many fields, authorship is distributed among large teams of researchers, some of whom, including paradigmatic senior authors who guide and determine the scope of a project and ultimately vouch for its integrity, may not write a single word. In this paper, we argue that LLM use (under specific conditions) is analogous to a form of senior authorship. On this view, the use of LLMs, even to generate complete drafts of research papers, can be considered a legitimate form of authorship according to the accepted criteria in many fields. We conclude that either such use should be recognized as legitimate, or current criteria for authorship require fundamental revision. AI use declaration: GPT-5 was used to help format Box 1. AI was not used for any other part of the preparation or writing of this manuscript.

cs.CY↗

arXiv2025

Development of Application-Specific Large Language Models to Facilitate Research Ethics Review

Institutional review boards (IRBs) play a crucial role in ensuring the ethical conduct of human subjects research, but face challenges including inconsistency, delays, and inefficiencies. We propose the development and implementation of application-specific large language models (LLMs) to facilitate IRB review processes. These IRB-specific LLMs would be fine-tuned on IRB-specific literature and institutional datasets, and equipped with retrieval capabilities to access up-to-date, context-relevant information. We outline potential applications, including pre-review screening, preliminary analysis, consistency checking, and decision support. While addressing concerns about accuracy, context sensitivity, and human oversight, we acknowledge remaining challenges such as over-reliance on AI and the need for transparency. By enhancing the efficiency and quality of ethical review while maintaining human judgment in critical decisions, IRB-specific LLMs offer a promising tool to improve research oversight. We call for pilot studies to evaluate the feasibility and impact of this approach.

cs.CL↗

arXiv2025

Relational Norms for Human-AI Cooperation

How we should design and interact with social artificial intelligence depends on the socio-relational role the AI is meant to emulate or occupy. In human society, relationships such as teacher-student, parent-child, neighbors, siblings, or employer-employee are governed by specific norms that prescribe or proscribe cooperative functions including hierarchy, care, transaction, and mating. These norms shape our judgments of what is appropriate for each partner. For example, workplace norms may allow a boss to give orders to an employee, but not vice versa, reflecting hierarchical and transactional expectations. As AI agents and chatbots powered by large language models are increasingly designed to serve roles analogous to human positions - such as assistant, mental health provider, tutor, or romantic partner - it is imperative to examine whether and how human relational norms should extend to human-AI interactions. Our analysis explores how differences between AI systems and humans, such as the absence of conscious experience and immunity to fatigue, may affect an AI's capacity to fulfill relationship-specific functions and adhere to corresponding norms. This analysis, which is a collaborative effort by philosophers, psychologists, relationship scientists, ethicists, legal experts, and AI researchers, carries important implications for AI systems design, user behavior, and regulation. While we accept that AI systems can offer significant benefits such as increased availability and consistency in certain socio-relational roles, they also risk fostering unhealthy dependencies or unrealistic expectations that could spill over into human-human relationships. We propose that understanding and thoughtfully shaping (or implementing) suitable human-AI relational norms will be crucial for ensuring that human-AI interactions are ethical, trustworthy, and favorable to human well-being.

cs.AI↗

arXiv2019

Priming intuition disfavors instrumental harm but not impartial beneficence

Understanding the cognitive underpinnings of moral judgment is one of most pressing problems in psychological science. Some highly-cited studies suggest that reliance on intuition decreases utilitarian (expected welfare maximizing) judgments in sacrificial moral dilemmas in which one has to decide whether to instrumentally harm (IH) one person to save a greater number of people. However, recent work suggests that such dilemmas are limited in that they fail to capture the positive, defining core of utilitarianism: commitment to impartial beneficence (IB). Accordingly, a new two-dimensional model of utilitarian judgment has been proposed that distinguishes IH and IB components. The role of intuition on this new model has not been studied. Does relying on intuition disfavor utilitarian choices only along the dimension of instrumental harm or does it also do so along the dimension of impartial beneficence? To answer this question, we conducted three studies (total N = 970, two preregistered) using conceptual priming of intuition versus deliberation on moral judgments. Our evidence converges on an interaction effect, with intuition decreasing utilitarian judgments in IH - as suggested by previous work - but failing to do so in IB. These findings bolster the recently proposed two-dimensional model of utilitarian moral judgment, and point to new avenues for future research.

physics.soc-ph↗