Digital Commons Network^™

Algorithmic Tax Ownership, Young Ran (Christine) Kim, Dmitry Erokhin

Articles

Tax ownership is a crucial concept for determining tax liabilities, compliance, and enforcement. However, neither the courts nor the IRS has provided clear guidance on how to analyze it. Since the Supreme Court first outlined a twenty-six-factor test for determining tax ownership in Frank Lyon Co. v. United States in 1978, this multifactor test has remained largely unchanged, and there has been no further guidance from the courts or the IRS to this day. Even tests with shorter lists of factors only add to the confusion regarding compliance and enforcement, as there is no clarity on which factors are most …

Ai, Medicine, And Social Determinants Of Health Data, Ryan Doyloo, Nicholson Price

Articles

This article examines how medical AI systems are incorporating SDoH data and the governance challenges that follow. The authors show that while SDoH integration can enhance clinical workflows and predictive accuracy — potentially improving outcomes for underserved populations — it also introduces acute risks of proxy discrimination, where facially neutral variables replicate protected characteristics. Surveying U.S., EU, and international frameworks, the authors argue that existing regimes lack clear ex ante guidance to distinguish beneficial from harmful uses of SDoH data. In response, they advance post-market monitoring as a pragmatic and scalable pathway: generating real-world, SDoH-stratified evidence that can support enforcement, …

Technology Before, During, And After Incarceration: Current Product Landscape, Sociotechnical Concerns, And Legal Considerations In The U.S. Context, Yael Eiger, Taylor Hansen, Teanna Barrett, Jevan Hutson, Bryce Clayton Newell, Franziska Roesner

Articles

Emerging technology, including AI, is proliferating throughout the U.S. carceral system. These technologies are marketed to prisons and police departments and then procured using taxpayer money. Previous investigative reporting has exposed troubling kickback schemes, unconstitutional data collection practices, and biased algorithmic outcomes in a handful of prominent technologies (e.g., Flock, Palantir, Clearview AI, COMPAS). In this work, we consider the broader ecosystem of carceral technologies: we catalog 122 products from 53 companies selling technology to carceral institutions. In a collaboration among computer science, law, and surveillance studies scholars, we surface sociotechnical, ethical, and legal concerns related to the use and …

Racing To Safety: Tax Policy For Ai Safety-By-Design, Mirit Eyal, Yonathan Arbel

Articles

The White House recently announced its vision of artificial intelligence (AI) policy: AI development is a race and America must win it. To that end, a new America's AI Action Plan directs federal agencies and states to remove regulatory barriers to AI development and accelerate innovation. This approach leaves limited room for regulatory measures that would address the safety risks of powerful AI systems: their behavior in novel domains remains unpredictable, their decision-making opaqueness, and their alignment with human values is uncertain. While experts warn of large-scale accidents, policymakers find themselves in a bind: Regulate AI and cede ground to …

Saturated Hierarchical Atomic Incremental Learning (Shail): A Behavioral Learning Perspective On Staged Mastery And Saturation, Ernest Fokoue

Articles

We introduce Saturated Hierarchical Atomic Incremental Learning (sHAIL), a learning paradigm in which complex tasks are approached through a sequence of simpler atomic subtasks, each mastered to saturation before progression. The central mechanism is a saturation criterion that detects when learning dynamics enter a plateau region, triggering consolidation and subsequent ascent to a higher level of task complexity. We develop a theoretical framework for sHAIL and show that it naturally gives rise to \emph{staircased convergence}: alternating phases of rapid improvement and genuine plateau. Within each level, classical convergence guarantees apply under standard smoothness conditions, while the hierarchical transitions are driven …

No Intelligence Without Statistics: The Invisible Backbone Of Artificial Intelligence, Ernest Fokoue

Articles

The rapid ascent of artificial intelligence (AI) is often portrayed as a revolution born from computer science and engineering. This narrative, however, obscures a fundamental truth: the theoretical and methodological core of AI is, and has always been, statistical. This paper systematically argues that the field of statistics provides the indispensable foundation for machine learning and modern AI. We deconstruct AI into nine foundational pillars—Inference, Density Estimation, Sequential Learning, Generalization, Representation Learning, Interpretability, Causality, Optimization, and Unification—demonstrating that each is built upon century-old statistical principles. From the inferential frameworks of hypothesis testing and estimation that underpin model evaluation, to the …

Decorrelation, Diversity, And Emergent Intelligence: The Isomorphism Between Social Insect Colonies And Ensemble Machine Learning, Ernest Fokoue, Gregory Babbitt, Yuval Levental

Articles

Social insect colonies and ensemble machine learning methods represent two of the most successful examples of decentralized information processing in nature and computation respectively. Here we develop a rigorous mathematical framework demonstrating that ant colony decision-making and random forest learning are isomorphic under a common formalism of stochastic ensemble intelligence. We show that the mechanisms by which genetically identical ants achieve functional differentiation— through stochastic response to local cues and positive feedback—map precisely onto the bootstrap aggregation and random feature subsampling that decorrelate decision trees. Using tools from Bayesian inference, multi-armed bandit theory, and statistical learning theory, we prove that …

A General Weighting Theory For Ensemble Learning: Beyond Variance Reduction Via Spectral And Geometric Structure, Ernest Fokoue

Articles

Ensemble learning is traditionally justified as a variance-reduction strategy, explaining its strong performance for unstable predictors such as decision trees. This explanation, however, does not account for ensembles constructed from intrinsically stable estimators-including smoothing splines, kernel ridge regression, Gaussian process regression, and other regularized reproducing kernel Hilbert space (RKHS) methods whose variance is already tightly controlled by regularization and spectral shrinkage. This paper develops a general weighting theory for ensemble learning that moves beyond classical variance-reduction arguments. We formalize ensembles as linear operators acting on a hypothesis space and endow the space of weighting sequences with geometric and spectral constraints. …

On The Scientific Stature Of Data Science: The Epistemological Unicorn, Ernest Fokoue

Articles

Data Science has ignited unprecedented academic, industrial, and pedagogical fervor, yet its status as a \textit{science} in the classical sense---comparable to physics or biology---remains profoundly unsettled. This article interrogates the epistemological foundations of Data Science by examining its hybrid theoretical lineage, from the Universal Approximation Theorem to the No-Free-Lunch Theorems, with special emphasis on the fundamental Bayesian optimality results for both regression and classification. We argue that Data Science is in a vigorous \textit{gestational period}, characterized not by an absence of principles but by a creative tension between empirical pragmatism and deep mathematical theory. The Cross-Validation score emerges as the …

On Fibonacci Ensembles: An Alternative Approach To Ensemble Learning Inspired By The Timeless Architecture Of The Golden Ratio, Ernest Fokoue

Articles

Nature rarely reveals her secrets bluntly, yet in the Fibonacci sequence she grants us a glimpse of her quiet architecture of growth, harmony, and recursive stability \citep{Koshy2001Fibonacci, Livio2002GoldenRatio}. From spiral galaxies to the unfolding of leaves, this humble sequence reflects a universal grammar of balance. In this work, we introduce \emph{Fibonacci Ensembles}, a mathematically principled yet philosophically inspired framework for ensemble learning that complements and extends classical aggregation schemes such as bagging, boosting, and random forests \citep{Breiman1996Bagging, Breiman2001RandomForests, Friedman2001GBM, Zhou2012Ensemble, HastieTibshiraniFriedman2009ESL}. Two intertwined formulations unfold: (1) the use of normalized Fibonacci weights -- tempered through orthogonalization and Rao--Blackwell optimization -- …

Fibonacci-Driven Recursive Ensembles: Algorithms, Convergence, And Learning Dynamics, Ernest Fokoue

Articles

This paper develops the algorithmic and dynamical foundations of recursive ensemble learning driven by Fibonacci-type update flows. In contrast with classical boosting Freund and Schapire (1997); Friedman (2001), where the ensemble evolves through first-order additive updates, we study second-order recursive architectures in which each predictor depends on its two immediate predecessors. These Fibonacci flows induce a learning dynamic with memory, allowing ensembles to integrate past structure while adapting to new residual information. We introduce a general family of recursive weight-update algorithms encompassing Fibonacci, tribonacci, and higher-order recursions, together with continuous-time limits that yield systems of differential equations governing ensemble evolution. …

Learning Ordinal Geometry: Semantic–Aware Kernels For Ordered Categorical Data, Ernest Fokoue

Articles

Ordinal data arise ubiquitously in survey research, psychology, medicine, economics, and recommender systems, yet kernel methods for such data typically rely on either nominal encodings or arbitrary numeric codings. The former discards order information; the lat- ter imposes a fictitious metric structure. This paper develops a principled framework for kernel design on ordinal scales and introduces a new class of Semantic–Aware Ordinal Ker- nels (SAOK) that simultaneously capture ordinal order and semantic proximity between categories. We begin by formalizing order–preserving embeddings of finite chains and characterizing a broad family of chain distances that are conditionally negative definite. Through Schoen- berg …

Factors For Patient Trust And Acceptance Of Medical Artificial Intelligence, Ana Bracic, Kayte Spector-Bagdady, Sophie Towle, Rina Zhang, Cornelius A. James, Nicholson W. Price Ii

Articles

Artificial intelligence (AI) is increasingly used in clinical care, but widespread adoption requires patient trust. Trust may be enhanced through systemic governance mechanisms or frontline clinicians providing a human in the loop for AI oversight. However, it is unclear how different approaches specifically influence patient trust in the use of medical AI. The objective is to determine the extent to which patient trust in and choice of medical scenarios involving AI are associated with governance mechanisms, clinician presence, performance, and data quality.

Selecting Without Replacement From A Population Of Bands Of Serially Connected Objects, James E. Marengo, Dominick Banasik, Joseph Voelkel, David L. Farnsworth

Articles

The sampling procedure from a finite population of objects that are serially attached into bands is described and analyzed. One object is randomly selected and removed at a time, which results in that object’s band being broken into two bands or shortened by one object. The main result gives the probability of choosing an object that is part of a band of serially connected objects of any specified size at each stage of the selection process.

Law And The Self-Coordinating Market Idea, Sanjukta Paul

Articles

Much of the focus of the live Symposium was on comparing existing scholarship associated with two intellectual communities. I have no objection to that enterprise in the abstract, though I think it is a bit premature where law and political economy (LPE) is concerned and sets up an apples-to-oranges comparison to the decades-old streams of work and thinking in law and economics (L&E). But I would rather use the privilege of the space in this written Symposium to sketch what I believe is the ultimate substantive nub of contestation in this conversation about the core subject matter of “the economy” …

Ai-Powered Lawyering: Ai Reasoning Models, Retrieval Augmented Generation, And The Future Of Legal Practice, Daniel Benjamin Schwarcz, Sam Manning, J.J. Prescott, Patrick Barry, David R. Cleveland, Beverly Rich

Articles

Generative AI is set to transform the legal profession, though its most promising uses and ultimate effects are still unclear. While AI models like GPT-4 improve efficiency, they can also “hallucinate” and may undermine legal judgment, particularly in complex tasks typically handled by skilled lawyers. This article examines two emerging AI innovations that may mitigate these concerns: Retrieval Augmented Generation (RAG), which grounds AI-powered analysis in legal sources, and AI reasoning models, which structure complex reasoning before generating output. We conduct the first randomized controlled trial assessing these technologies, assigning upper-level law students to complete legal tasks using a RAG-powered …

Grading Machines: Can Ai Exam-Grading Replace Law Professors?, Kevin L. Cope, Jens Frankenreiter, Scott Hirst, Eric A. Posner, Daniel Schwarcz, Dane Thorley

Articles

In the past few years, large language models (LLMs) have achieved significant technical advances, enabling legal-advocacy organizations to adopt them as complements to—or substitutes for—lawyers and other human experts. The role of LLMs in legal education, however, is underexplored. While several studies have examined LLMs’ performance in taking law school exams, finding mixed results, there have been no published studies systematically analyzing LLMs’ competence at one of law professors’ chief responsibilities: grading law school exams. This paper presents results of an analysis of how LLMs perform in evaluating student responses to legal analysis questions of the kind typically contained in …

State Climate Superfunds, Rachel Rothschild

Articles

The harmful effects of climate change have already arrived in cities and states across America, with disasters increasing markedly in recent years along with more gradual environmental changes like sea-level rise and drought. To protect populations and natural resources, significant funding will be necessary for preventative measures as well as disaster response.

At present, it is states and ordinary taxpayers who must shoulder the enormous costs and planning for climate adaptation. A number of state legislators, however, have recently proposed enacting new laws that would require the companies who have most profited from fossil fuel usage to assist in funding …

Ai-Powered Lawyering: Ai Reasoning Models, Retrieval Augmented Generation, And The Future Of Legal Practice, Daniel Schwarcz, Sam Manning, J. J. Prescott, Patrick Barry, David R. Cleveland, Beverly Rich

Articles

Generative AI is set to transform the legal profession, though its most promising uses and ultimate effects are still unclear. While AI models like GPT-4 improve efficiency, they can also “hallucinate” and may undermine legal judgment, particularly in complex tasks typically handled by skilled lawyers. This article examines two emerging AI innovations that may mitigate these concerns: Retrieval Augmented Generation (RAG), which grounds AI-powered analysis in legal sources, and AI reasoning models, which structure complex reasoning before generating output. We conduct the first randomized controlled trial assessing these technologies, assigning upper-level law students to complete legal tasks using a RAG-powered …

Monitoring, Oversight, And Learning In Medical Ai, W. Nicholson Price Ii

Articles

When medical AI errs, it often goes unnoticed. If there’s a specific patient injury, and the link to AI is obvious, that problem might be reported to the Food and Drug Administration (FDA), but not always. And many other types of problems, like worse performance on specific groups or ineffective integration into health system workflows, simply don’t fall within the contours of regularized reporting. Even if they are noticed by the health system—far from a given—there’s no obvious way to share that information more broadly. Against this backdrop, there are justified calls for better oversight and reporting. But there’s the …

Expanding Astrobiology: The Case For A Lunar Biorepository, Mary Hagedorn, Lynne R. Parenti, Robert A. Craddock, Pierre Comizzoli, Paula Mabee, Bonnie Meinke, Susan Wolf, John C. Bischof, Rebecca D. Sandlin, Shannon N. Tessier, Mahmet Toner, Baptiste Journaux, Robert Ambrose, Garret Fitzpatrick

Articles

Earth’s resources are essential to support an expanding presence beyond the planet. Yet global conflicts, environmental change, and natural disasters threaten ecosystems and biodiversity, putting the integrity of Earth’s ecosystems and its resources at risk. These converging challenges underscore the urgency to develop innovative strategies to conserve Earth’s biodiversity. Astrobiology—seeking to understand life’s origins, limits, and potential beyond Earth—plays a central role in this effort, helping to preserve Earth’s species while also providing critical assets to explore and work in space. A Lunar Biorepository was proposed to hold cryopreserved samples from among the most critical species on Earth. Here, we …

Forget Me Not? Machine Unlearning’S Implications For Privacy Law, Jevan Hutson, Cedric Whitney, Jay T. Conrad

Articles

Generative AI systems are increasingly relied on and are already actively reshaping how we think about privacy and data protection law. Models ingest and process vast amounts of personal and sensitive data, challenging assurances of compliance with legal frameworks like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) with increasing intensity. Machine unlearning is an emerging tool in practitioners’ attempts to address these challenges: the act of selectively removing or suppressing specific data, such as personal data that a data subject requests be deleted, from AI models as means of complying with legal obligations or …

An Incidental Standard For Medical Ai, Nicholson W. Price Ii

Articles

Medical AI is poised to make a major difference in the provision of health. It brings major challenges, though: how can developers and implementers ensure that it will work safely and effectively—especially within the context of complex and highly variable health-care systems? Standards provide one key tool, potentially providing guidelines for everything from privacy to accuracy to how AI interacts with human clinicians. This Essay considers the last of these, describing a powerful quasi-standard from a surprising source: an FDA guidance document that tells developers when certain AI systems are not considered medical devices, and are therefore not regulated by …

Bending Energy Schemes For Discrete-Spring-Network Structural Modelling Of Red Blood Cells, Osayomwanbor Ehi-Egharevba, Mingzhu Chen, Fergal Boyle

Articles

Red blood cells (RBCs) undergo large structural deformation, including bending, when passing through capillaries. They also exhibit a range of complex shapes such as stomatocytes, discocytes and echinocytes that form due to altered blood pH and salt levels, ingested drugs and adenosine triphosphate depletion. Discrete-spring-network structural models of RBCs employ different numerical treatments of the continuum bending energy. This affects bending accuracy and the prediction of accurate RBC shapes. This research compares three representations called bending energy scheme (BES) A, B and C to evaluate their accuracy in shape predictions. BES A, seen throughout the literature, is based on the …

The Active Classroom: A Narrative Review Of Active Teaching Methods And The Flipped Classroom Model In Graduate Medical Education., Kalyan Kandra, Praneetha Vennam

Articles

Graduate medical education (GME) is undergoing a significant pedagogical transformation, moving away from traditional, passive learning environments toward more dynamic, learner-centered approaches. This narrative review examines the implementation and impact of active teaching methods in GME, with a specific focus on the flipped classroom model. In this narrative review, we compare and contrast these innovative strategies with traditional didactic lectures, evaluating their effects on learner engagement, knowledge retention, clinical reasoning, and overall satisfaction. Active learning, grounded in constructivist theory, repositions the resident as an active participant in their education, utilizing methods such as case-based learning, team-based learning, and simulation. The …

Artificial Ignorance: Understanding The Role Of Ai In Modern Agnotology, Amit Ray, Michael Nolan

Articles

This paper explores the concept of agnotology, the deliberate production of ignorance, within the context of modern scientific endeavors, particularly in the corporate and technological sectors. It examines how industries use various tactics to manipulate public understanding of scientific issues, often to protect profits and limit liability. The rise of private sector funding and the increasing reliance on technologies like AI and machine learning have exacerbated this process by making scientific inquiry more opaque and less accountable. Ultimately, we argue that as knowledge production becomes more entangled with corporate interests and technological systems, traditional methods of oversight and regulation are …

The Bandages Problem, James E. Marengo, Joseph G. Voelkel, David L. Farnsworth

Articles

A new probability problem, named the Bandages Problem, is described and solved. The problem involves repeatedly selecting and removing an item at random from a finite population that initially consists of a known configuration of single and paired items. For each selection, the probability that the chosen item is single is found. Generalizations are suggested.

Comparing Conventional And Alternative Mechanisms Of Discovering And Accessing The Scientific Literature, William H. Walters

Articles

This study compares the bibliographic and full-text coverage of 15 conventional and alternative discovery/access mechanisms: two multidisciplinary library databases (Scopus and the Web of Science Core Collection), five single-subject databases, the integrated library search (ILS) mechanism of Manhattan University, a scholarly search engine (Google Scholar), two web-based scholarly databases (Dimensions and OpenAlex), two academic social networks (Academia.edu and ResearchGate), and two pirate sites (Anna’s Archive and Sci-Hub). The analysis is based on known-item searches for 875 target documents in chemistry, materials science, cardiology, public health, economics, education, and psychology. Overall, Google Scholar, OpenAlex, and the ILS are the most comprehensive …

The Improvement Regime: Public Trusts, Real Estates, And India’S Urban Futurities, Anwesha Ghosh

Articles

Over the last two decades, since scholarly writing on India witnessed an “urban turn,” numerous historians have analyzed the role of the improvement trust in the redevelopment of Indian cities in the twentieth century, most specifically those of Bombay, Calcutta, and Delhi. This paper revisits and reassesses some of their key arguments to suggest that rather than studying the “failures” of the individual trusts to foster sanitary built environments, we should pay attention to the contingent workings of the city trusts that were constitutively designed for such failures. Using a comparative analysis of the Bombay and Calcutta improvement trusts, this …

Shrinkage Study In Photopolymerisable Hybrid Sol-Gel Through Holographic Patterning, Jamshed Aftab, Izabela Naydenova, Tatsiana Mikulchyk

Articles

Photopolymerisation induced shrinkage of holographic materials is one of the main factors which needs to be considered for designing holographic optical elements (HOEs) with high accuracy in light redirection with maximum efficiency. This work studies the shrinkage in photopolymerisable hybrid sol-gel (PHSG) by examining the properties of volume transmission gratings recorded in PHSG layers. It explores both the dependence of shrinkage on the holographic grating parameters (thickness, spatial frequency, slant angle) and the effect of material aging. By using the fringe-plane rotation model, shrinkage is found to have the maximum value of 1.37 % at 765 lines/mm (19.36° slant angle) …