Hasil untuk "Doctrinal Theology"

Navya SD, Sreekanth D, SS Uma Sankari

As financial institutions increasingly adopt machine learning for credit risk assessment, the persistence of algorithmic bias remains a critical barrier to equitable financial inclusion. This study provides a comprehensive audit of structural gender bias within the Taiwan Credit Default dataset, specifically challenging the prevailing doctrine of "fairness through blindness." Despite the removal of explicit protected attributes and the application of industry standard fairness interventions, our results demonstrate that gendered predictive signals remain deeply embedded within non-sensitive features. Utilizing SHAP (SHapley Additive exPlanations), we identify that variables such as Marital Status, Age, and Credit Limit function as potent proxies for gender, allowing models to maintain discriminatory pathways while appearing statistically fair. To mathematically quantify this leakage, we employ an adversarial inverse modeling framework. Our findings reveal that the protected gender attribute can be reconstructed from purely non-sensitive financial features with an ROC AUC score of 0.65, demonstrating that traditional fairness audits are insufficient for detecting implicit structural bias. These results advocate for a shift from surface-level statistical parity toward causal-aware modeling and structural accountability in financial AI.

Noam Kolt

The prospect of artificial superintelligence -- AI agents that can generally outperform humans in cognitive tasks and economically valuable activities -- will transform the legal order as we know it. Operating autonomously or under only limited human oversight, AI agents will assume a growing range of roles in the legal system. First, in making consequential decisions and taking real-world actions, AI agents will become de facto subjects of law. Second, to cooperate and compete with other actors (human or non-human), AI agents will harness conventional legal instruments and institutions such as contracts and courts, becoming consumers of law. Third, to the extent AI agents perform the functions of writing, interpreting, and administering law, they will become producers and enforcers of law. These developments, whenever they ultimately occur, will call into question fundamental assumptions in legal theory and doctrine, especially to the extent they ground the legitimacy of legal institutions in their human origins. Attempts to align AI agents with extant human law will also face new challenges as AI agents will not only be a primary target of law, but a core user of law and contributor to law. To contend with the advent of superintelligence, lawmakers -- new and old -- will need to be clear-eyed, recognizing both the opportunity to shape legal institutions as society braces for superintelligence and the reality that, in the longer run, this may be a joint human-AI endeavor.

Sonia Katyal, Aniket Kesari

Almost every industry today confronts the potential role of artificial intelligence and machine learning in its future. While many studies examine AI in consumer marketing, less attention addresses AI's role in creating and selecting trademarks that are distinctive, recognizable, and meaningful to consumers. Traditional economic approaches to trademarks focus almost exclusively on consumer-based, demand-side considerations regarding search. However, these approaches are incomplete because they fail to account for substantial costs faced not just by consumers, but by trademark applicants as well. Given AI's rapidly increasing role in trademark search and similarity analysis, lawyers and scholars should understand its dramatic implications. This paper proposes that AI should interest anyone studying trademarks and their role in economic decision-making. We examine how machine learning techniques will transform the application and interpretation of foundational trademark doctrines, producing significant implications for the trademark ecosystem. We run empirical experiments regarding trademark search to assess the efficacy of various trademark search engines, many of which employ machine learning methods. Through comparative analysis, we evaluate how these AI-powered tools function in practice. In an age where artificial intelligence increasingly governs trademark selection, the classic division between consumers and trademark owners deserves an updated, supply-side framework. This insight has transformative potential for encouraging both innovation and efficiency in trademark law and practice.

Shiping Chen, Qin Wang, Guangsheng Yu et al.

Open agentic systems combine LLM-based planning with external capabilities, persistent memory, and privileged execution. They are used in coding assistants, browser copilots, and enterprise automation. OpenClaw is a visible instance of this broader class. Without much attention yet, their security challenge is fundamentally different from that of traditional software that relies on predictable execution and well-defined control flow. In open agentic systems, everything is ''probabilistic'': plans are generated at runtime, key decisions may be shaped by untrusted natural-language inputs and tool outputs, execution unfolds in uncertain environments, and actions are taken under authority delegated by human users. The central challenge is therefore not merely robustness against individual attacks, but the governance of agentic behavior under persistent uncertainty. This paper systematizes the area through a software engineering lens. We introduce a six-dimensional analytical taxonomy and synthesize 50 papers spanning attacks, benchmarks, defenses, audits, and adjacent engineering foundations. From this synthesis, we derive a reference doctrine for secure-by-construction agent platforms, together with an evaluation scorecard for assessing platform security posture. Our review shows that the literature is relatively mature in attack characterization and benchmark construction, but remains weak in deployment controls, operational governance, persistent-memory integrity, and capability revocation. These gaps define a concrete engineering agenda for building agent ecosystems that are governable, auditable, and resilient under compromise.

Jake Robertson, Noah Hollmann, Samuel Müller et al.

Machine learning (ML) systems are utilized in critical sectors, such as healthcare, law enforcement, and finance. However, these systems are often trained on historical data that contains demographic biases, leading to ML decisions that perpetuate or exacerbate existing social inequalities. Causal fairness provides a transparent, human-in-the-loop framework to mitigate algorithmic discrimination, aligning closely with legal doctrines of direct and indirect discrimination. However, current causal fairness frameworks hold a key limitation in that they assume prior knowledge of the correct causal model, restricting their applicability in complex fairness scenarios where causal models are unknown or difficult to identify. To bridge this gap, we propose FairPFN, a tabular foundation model pre-trained on synthetic causal fairness data to identify and mitigate the causal effects of protected attributes in its predictions. FairPFN's key contribution is that it requires no knowledge of the causal model and still demonstrates strong performance in identifying and removing protected causal effects across a diverse set of hand-crafted and real-world scenarios relative to robust baseline methods. FairPFN paves the way for promising future research, making causal fairness more accessible to a wider variety of complex fairness problems.

Noam Kolt

The field of AI is undergoing a fundamental transition from generative models that can produce synthetic content to artificial agents that can plan and execute complex tasks with only limited human involvement. Companies that pioneered the development of language models have now built AI agents that can independently navigate the internet, perform a wide range of online tasks, and increasingly serve as AI personal assistants and virtual coworkers. The opportunities presented by this new technology are tremendous, as are the associated risks. Fortunately, there exist robust analytic frameworks for confronting many of these challenges, namely, the economic theory of principal-agent problems and the common law doctrine of agency relationships. Drawing on these frameworks, this Article makes three contributions. First, it uses agency law and theory to identify and characterize problems arising from AI agents, including issues of information asymmetry, discretionary authority, and loyalty. Second, it illustrates the limitations of conventional solutions to agency problems: incentive design, monitoring, and enforcement might not be effective for governing AI agents that make uninterpretable decisions and operate at unprecedented speed and scale. Third, the Article explores the implications of agency law and theory for designing and regulating AI agents, arguing that new technical and legal infrastructure is needed to support governance principles of inclusivity, visibility, and liability.

Christoph Schuhmann, Gollam Rabby, Ameya Prabhu et al.

Paywalls, licenses and copyright rules often restrict the broad dissemination and reuse of scientific knowledge. We take the position that it is both legally and technically feasible to extract the scientific knowledge in scholarly texts. Current methods, like text embeddings, fail to reliably preserve factual content, and simple paraphrasing may not be legally sound. We propose a new idea for the community to adopt: convert scholarly documents into knowledge preserving, but style agnostic representations we term Knowledge Units using LLMs. These units use structured data capturing entities, attributes and relationships without stylistic content. We provide evidence that Knowledge Units (1) form a legally defensible framework for sharing knowledge from copyrighted research texts, based on legal analyses of German copyright law and U.S. Fair Use doctrine, and (2) preserve most (~95\%) factual knowledge from original text, measured by MCQ performance on facts from the original copyrighted text across four research domains. Freeing scientific knowledge from copyright promises transformative benefits for scientific research and education by allowing language models to reuse important facts from copyrighted text. To support this, we share open-source tools for converting research documents into Knowledge Units. Overall, our work posits the feasibility of democratizing access to scientific knowledge while respecting copyright.

Volkan Ustun, Soham Hans, Rajay Kumar et al.

Multi-agent reinforcement learning (MARL) is increasingly ubiquitous in training dynamic and adaptive synthetic characters for interactive simulations on geo-specific terrains. Frameworks such as Unity's ML-Agents help to make such reinforcement learning experiments more accessible to the simulation community. Military training simulations also benefit from advances in MARL, but they have immense computational requirements due to their complex, continuous, stochastic, partially observable, non-stationary, and doctrine-based nature. Furthermore, these simulations require geo-specific terrains, further exacerbating the computational resources problem. In our research, we leverage Unity's waypoints to automatically generate multi-layered representation abstractions of the geo-specific terrains to scale up reinforcement learning while still allowing the transfer of learned policies between different representations. Our early exploratory results on a novel MARL scenario, where each side has differing objectives, indicate that waypoint-based navigation enables faster and more efficient learning while producing trajectories similar to those taken by expert human players in CSGO gaming environments. This research points out the potential of waypoint-based navigation for reducing the computational costs of developing and training MARL models for military training simulations, where geo-specific terrains and differing objectives are crucial.

M. Z. Naser

Despite the widespread interest in machine learning (ML), the engineering industry has not yet fully adopted ML-based methods, which has left engineers and stakeholders uncertain about the legal and regulatory frameworks that govern their decisions. This gap remains unaddressed as an engineer's decision-making process, typically governed by professional ethics and practical guidelines, now intersects with complex algorithmic outputs. To bridge this gap, this paper explores how engineers can navigate legal principles and legislative justifications that support and/or contest the deployment of ML technologies. Drawing on recent precedents and experiences gained from other fields, this paper argues that analogical reasoning can provide a basis for embedding ML within existing engineering codes while maintaining professional accountability and meeting safety requirements. In exploring these issues, the discussion focuses on established liability doctrines, such as negligence and product liability, and highlights how courts have evaluated the use of predictive models. We further analyze how legislative bodies and standard-setting organizations can furnish explicit guidance equivalent to prior endorsements of emergent technologies. This exploration stresses the vitality of understanding the interplay between technical justifications and legal precedents for shaping an informed stance on ML's legitimacy in engineering practice. Finally, our analysis catalyzes a legal framework for integrating ML through which stakeholders can critically assess the responsibilities, liabilities, and benefits inherent in ML-driven engineering solutions.

Hang Jiang, Xiajie Zhang, Robert Mahari et al.

Making legal knowledge accessible to non-experts is crucial for enhancing general legal literacy and encouraging civic participation in democracy. However, legal documents are often challenging to understand for people without legal backgrounds. In this paper, we present a novel application of large language models (LLMs) in legal education to help non-experts learn intricate legal concepts through storytelling, an effective pedagogical tool in conveying complex and abstract concepts. We also introduce a new dataset LegalStories, which consists of 294 complex legal doctrines, each accompanied by a story and a set of multiple-choice questions generated by LLMs. To construct the dataset, we experiment with various LLMs to generate legal stories explaining these concepts. Furthermore, we use an expert-in-the-loop approach to iteratively design multiple-choice questions. Then, we evaluate the effectiveness of storytelling with LLMs through randomized controlled trials (RCTs) with legal novices on 10 samples from the dataset. We find that LLM-generated stories enhance comprehension of legal concepts and interest in law among non-native speakers compared to only definitions. Moreover, stories consistently help participants relate legal concepts to their lives. Finally, we find that learning with stories shows a higher retention rate for non-native speakers in the follow-up assessment. Our work has strong implications for using LLMs in promoting teaching and learning in the legal field and beyond.

Gregory M. Dickinson

The steady flow of social-media cases toward the Supreme Court shows a nation reworking its fundamental relationship with technology. The cases raise a host of questions ranging from difficult to impossible: how to nurture a vibrant public square when a few tech giants dominate the flow of information, how social media can be at the same time free from conformist groupthink and also protected against harmful disinformation campaigns, and how government and industry can cooperate on such problems without devolving toward censorship. To such profound questions, this Essay offers a comparatively modest contribution -- what not to do. Always the lawyer's instinct is toward analogy, considering what has come before and how it reveals what should come next. Almost invariably, that is the right choice. The law's cautious evolution protects society from disruptive change. But almost is not always, and, with social media, disruptive change is already upon us. Using social-media laws from Texas and Florida as a case study, this Essay shows how social-media's distinct features render it poorly suited to analysis by analogy and argues that courts should instead shift their attention toward crafting legal doctrines targeted to address social media's unique ills.

Micah Beck, Terry Moore

At the end of the 19th century the logician C.S. Peirce coined the term "fallibilism" for the "... the doctrine that our knowledge is never absolute but always swims, as it were, in a continuum of uncertainty and of indeterminacy". In terms of scientific practice, this means we are obliged to reexamine the assumptions, the evidence, and the arguments for conclusions that subsequent experience has cast into doubt. In this paper we examine an assumption that underpinned the development of the Internet architecture, namely that a loosely synchronous point-to-point datagram delivery service could adequately meet the needs of all network applications, including those which deliver content and services to a mass audience at global scale. We examine how the inability of the Networking community to provide a public and affordable mechanism to support such asynchronous point-to-multipoint applications led to the development of private overlay infrastructure, namely CDNs and Cloud networks, whose architecture stands at odds with the Open Data Networking goals of the early Internet advocates. We argue that the contradiction between those initial goals and the monopolistic commercial imperatives of hypergiant overlay infrastructure operators is an important reason for the apparent contradiction posed by the negative impact of their most profitable applications (e.g., social media) and strategies (e.g., targeted advertisement). We propose that, following the prescription of Peirce, we can only resolve this contradiction by reconsidering some of our deeply held assumptions.

Aloni Cohen, Sarah Scheffler, Mayank Varia

If a court knows that a respondent knows the password to a device, can the court compel the respondent to enter that password into the device? In this work, we propose a new approach to the foregone conclusion doctrine from Fisher v US that governs the answer to this question. The Holy Grail of this line of work would be a framework for reasoning about whether the testimony implicit in any action is already known to the government. In this paper we attempt something narrower. We introduce a framework for specifying actions for which all implicit testimony is, constructively, a foregone conclusion. Our approach is centered around placing the burden of proof on the government to demonstrate that it is not "rely[ing] on the truthtelling" of the respondent. Building on original legal analysis and using precise computer science formalisms, we propose demonstrability as a new central concept for describing compelled acts. We additionally provide a language for whether a compelled action meaningfully entails the respondent to perform in a manner that is 'as good as' the government's desired goal. Then, we apply our definitions to analyze the compellability of several cryptographic primitives including decryption, multifactor authentication, commitment schemes, and hash functions. In particular, our framework reaches a novel conclusion about compelled decryption in the setting that the encryption scheme is deniable: the government can compel but the respondent is free to use any password of her choice.

Jon Mentxakatorre Odriozola

Esta investigación explora las implicaciones antropológicas de la filosofía lingüística de J.R.R. Tolkien a la luz de la teología. A partir de selectos textos de Tolkien, se erige un marco interpretativo de su concepción y praxis artística, como clave de lectura de todo ejercicio poético –en tanto que poiesis–. El estudio de Génesis y Evangelio de San Juan ofrece la base para mostrar cómo la verdadera creación es siempre de carácter mítico, en tanto que fruto de una honda participación de la realidad, por lo que tal proceso es en verdad subcreación a partir de la Creación. El asombro ante el don del ser deriva en la exploración de la palabra al servicio de la Palabra, y el lugar antropológico del ser humano en tal relación. Con ello, se recoge y expone la relación entre la subcreación y el crecimiento o despliegue (physis) de la Creación. Abstract: The following text explores the anthropological implications of J.R.R. Tolkien's linguistic philosophy in the light of theology. From selected works of Tolkien, an interpretative framework of his artistic conception and praxis is constructed, as a key to understand any poetic activity – as poiesis. The study of Genesis and Gospel according to St John offers the basis to show how the real creation always has a mythic component, as the product of a deep participation of reality, so the process is in truth a subcreation from Creation. The astonishment before the gift of the being or thing concludes in the exploration of the word in service of the Word, and in the anthropological place of the human being in that relationship. Thanks to it, the text gathers and explains the relationship between subcreation and growing or unfolding (physis) of Creation.

Davide Trotta, Matteo Spadetto, Valeria de Paiva

Gödel's Dialectica interpretation was designed to obtain a relative consistency proof for Heyting arithmetic, to be used in conjunction with the double negation interpretation to obtain the consistency of Peano arithmetic. In recent years, proof theoretic transformations (so-called proof interpretations) that are based on Gödel's Dialectica interpretation have been used systematically to extract new content from proofs and so the interpretation has found relevant applications in several areas of mathematics and computer science. Following our previous work on Gödel fibrations, we present a (hyper)doctrine characterisation of the Dialectica which corresponds exactly to the logical description of the interpretation. To show that we derive in the category theory the soundness of the interpretation of the implication connective, as expounded on by Spector and Troelstra. This requires extra logical principles, going beyond intuitionistic logic, Markov's Principle (MP) and the Independence of Premise (IP) principle, as well as some choice. We show how these principles are satisfied in the categorical setting, establishing a tight (internal language) correspondence between the logical system and the categorical framework. This tight correspondence should come handy not only when discussing the applications of the Dialectica already known, like its use to extract computational content from (some) classical theorems (proof mining), its use to help to model specific abstract machines, etc. but also to help devise new applications.

Jefferson da Silva, Marcius Tadeu Maciel Nahur

Este artigo busca refletir sobre o problema do mal no pensamento de Agostinho (354-430) e no pensamento do filósofo francês Paul Ricoeur (1913-2005). Pensadores de épocas completamente distintas abordam em suas obras o problema do mal que sempre foi um desafio, tanto para a filosofia quanto para a teologia. Qual a origem do mal? Como teólogo e pastor de um povo, Agostinho responderá ao problema do mal baseado nas Sagradas Escrituras. Quanto a Ricoeur, sem a pretensão de a uma resposta para a origem do mal, convida seu leitor para seguir o caminho da reflexão, da prática e da espiritualização.

Simo Frestadius

ABSTRACT Baptism in the Holy Spirit has been central to classical Pentecostal traditions, but its ongoing importance in Pentecostal theology and practice is increasingly questioned. This article explores the common three ideas associated with the classical Pentecostal doctrine, namely, the beliefs that it is (1) a subsequent experience to conversion, (2) accompanied by tongues as initial evidence and (3) given for the purpose of empowerment for witness. The article does so by looking at the broader nature, signs, and purpose of Spirit baptism from a British and wider European classical Pentecostal perspective. The underlying argument is that the fullness of the Spirit experienced by the first generation of Pentecostals in Europe seems to have been spiritually deeper, theologically richer, and practically more inclusive than stated by some of the later doctrinal articulations. This has important theological implications for contemporary formulations of the Pentecostal doctrine of Spirit baptism.

Daniel Gratzer, Jonathan Sterling

We argue that locally Cartesian closed categories form a suitable doctrine for defining dependent type theories, including non-extensional ones. Using the theory of sketches, one may define syntactic categories for type theories in a style that resembles the use of Martin-Löf's Logical Framework, following the "judgments as types" principle. The concentration of type theories into their locally Cartesian closed categories of judgments is particularly convenient for proving syntactic metatheorems by semantic means (canonicity, normalization, etc.). Perhaps surprisingly, the notion of a context plays no role in the definitions of type theories in this sense, but the structure of a class of display maps can be imposed on a theory post facto wherever needed, as advocated by the Edinburgh school and realized by the %worlds declarations of the Twelf proof assistant. Uemura has proposed representable map categories together with a stratified logical framework for similar purposes. The stratification in Uemura's framework restricts the use of dependent products to be strictly positive, in contrast to the tradition of Martin-Löf's logical framework and Schroeder-Heister's analysis of higher-level deductions. We prove a semantic adequacy result for locally Cartesian closed categories relative to Uemura's representable map categories: if a theory is definable in the framework of Uemura, the locally Cartesian closed category that it generates is a conservative (fully faithful) extension of its syntactic representable map category. On this basis, we argue for the use of locally Cartesian closed categories as a simpler alternative to Uemura's representable map categories.

Bradley Butcher, Vincent S. Huang, Jeremy Reffin et al.

In solving real-world problems like changing healthcare-seeking behaviors, designing interventions to improve downstream outcomes requires an understanding of the causal links within the system. Causal Bayesian Networks (BN) have been proposed as one such powerful method. In real-world applications, however, confidence in the results of BNs are often moderate at best. This is due in part to the inability to validate against some ground truth, as the DAG is not available. This is especially problematic if the learned DAG conflicts with pre-existing domain doctrine. At the policy level, one must justify insights generated by such analysis, preferably accompanying them with uncertainty estimation. Here we propose a causal extension to the datasheet concept proposed by Gebru et al (2018) to include approximate BN performance expectations for any given dataset. To generate the results for a prototype Causal Datasheet, we constructed over 30,000 synthetic datasets with properties mirroring characteristics of real data. We then recorded the results given by state-of-the-art structure learning algorithms. These results were used to populate the Causal Datasheet, and recommendations were automatically generated dependent on expected performance. As a proof of concept, we used our Causal Datasheet Generation Tool (CDG-T) to assign expected performance expectations to a maternal health survey we conducted in Uttar Pradesh, India.

J. Butterfield, H. Gomes

This is the first of four papers prompted by a recent literature about a doctrine dubbed spacetime functionalism. This paper gives our general framework for discussing functionalism. Following Lewis, we take it as a species of reduction. We start by expounding reduction in a broadly Nagelian sense. Then we argue that Lewis's functionalism is an improvement on Nagelian reduction. This paper thereby sets the scene for the other papers, which will apply our framework to theories of space and time. Overall, we come to praise spacetime functionalism, not to bury it. But we criticize the recent philosophical literature for failing to stress: (i) functionalism's being a species of reduction (in particular: reduction of chrono-geometry to the physics of matter and radiation); (ii) functionalism's idea of specifying several concepts simultaneously by their roles; (iii) functionalism's providing bridge laws that are mandatory, not optional: they are statements of identity (or co-extension) that are conclusions of a deductive argument; and once we infer them, we have a reduction in a Nagelian sense. On the other hand, some of the older philosophical literature, and the mathematical physics literature, is faithful to these ideas (i) to (iii). In various papers, falling under various research programmes, the unique definability of a chrono-geometric concept (or concepts) in terms of matter and radiation, and a corresponding bridge law and reduction, is secured by a precise theorem. Hence our desire to celebrate these results as rigorous renditions of spacetime functionalism.