Nancy Abigail Nuñez Hernández

Este trabajo plantea la necesidad de restringir el uso de herramientas de inteligencia artificial (IA) como los modelos de lenguaje de gran tamaño (large language models, LLM), en las primeras etapas de la formación universitaria en humanidades. Para los estudiantes y profesionales de este campo, el pensamiento crítico es una herramienta fundamental, comparable al pensamiento matemático para profesionistas de ciencias o ingeniería. La evidencia científica muestra que el uso temprano de los LLM puede erosionar y obstaculizar el pensamiento crítico en usuarios jóvenes, por lo que es conveniente restringir su empleo durante la etapa formativa en humanidades.

This paper proposes that the free pragmatic enrichment process of narrowing can explain the context-sensitivity of knowledge attribution sentences, which is a central tenet of epistemic contextualism. Epistemic contextualism is a semantic thesis about the truth-conditions of knowledge attribution sentences of the form “S knows that p.”Even though it is a linguistic claim, it is proposed to solve an epistemic problem, i.e., the threat posed by Cartesian-style sceptical arguments. The rough idea behind epistemic contextualism’s solution to the sceptical problem is to argue that the premises of Cartesian-style sceptical arguments (specifically, the first one) generate a context in which the epistemic standards for knowledge attribution are extremely high and impossible to meet, leading to the sceptical conclusion: “S does not know that p”; under those standards, the sceptical conclusion seems to be true. But ordinarily, the negation of the sceptical conclusion (which is the sentence that asserts “S knows that p”) is evaluated as true because the epistemic standards operating in that context are loose.

This book features a unique selection of works presented at the 2019 annual international conference of the International Association for Computing and Philosophy (IACAP). Every contribution has been peer-reviewed, revised, and extended. The included chapters are thematically diverse; topics include epistemology, dynamic epistemic logic, topology, philosophy of science and computation, game theory and abductive inferences, automated reasoning and mathematical proofs, computer simulations, scientific modelling, applied ethics, pedagogy, human-robot interactions, and big data, algorithms, and artificial intelligence. The volume is a testament to the value of interdisciplinary approaches to the computational and informational turn. We live in a time of tremendous development, which requires rigorous reflection on the philosophical nature of these technologies and how they are changing the world. How can we understand these technologies? How do these technologies change our understanding of the world? And how do these technologies affect our place as humans in the world? These questions, and more, are addressed in this volume which is of interest to philosophers, engineers, and computer scientists alike.

Various notions of plausibility are used in cognitive science to argue for or against the “goodness of theories.” However, plausibility remains poorly understood and difcult to analyze. We review debates in the philosophy of science on uses of plausibility in the assessment of novel scientifc theories as well as recent attempts to formalize, reform, or eliminate specifc notions of plausibility. Although these discussions highlight important concerns behind plausibility claims, they fail to identify viable notions of plausibility that are sufciently diferent from other criteria of “good theory,” such as prior probability or external coherence. We survey uses of plausibility in linguistics and cognitive science, confrming that plausibility is often a proxy for other criteria of good theory. We argue that the need remains for concepts of plausibility that can be employed to assess the quality of proposals at the early stages of theory development when other criteria are not yet applicable. We identify two such notions: one relating to formal constraints on theories and another capturing initial epistemic consensus, if not necessarily convergence on the truth, about the target system in a community of inquiry. We briefy assess the specifcity and added value of these notions of plausibility relative to other criteria for good theory.

Una de las razones por las que los argumentos escépticos constituyen un problema es que generan una paradoja de intuiciones a favor de las premisas y en contra de la conclusión. Este conflicto de intuiciones es problemático porque se suscita ante un argumento deductivamente válido, de manera que hace falta explicar por qué parece que la consecuencia lógica de unas premisas verdaderas es falsa. Resolver el problema de los argumentos escépticos implica al menos dos retos: explicar la paradoja de intuiciones que estos argumentos generan y defender la idea de que las prácticas ordinarias de atribución de conocimiento son generalmente correctas.

In deduction the truth of the premises guarantees the truth of the conclusion because the conclusion is contained in the premises; because of that many philosophers have denied the possibility of gaining knowledge through deduction or through deductive reasoning, which leads to a problem known as the paradox of inference. To address this problem, I propose a distinction between deductive reasoning as a cognitive process capable of generating new knowledge, and implication as a logical relation between propositions, which accounts for deduction’s validity, and I present scientific evidence to underpin this proposal. En la deducción la verdad de las premisas garantiza la verdad de la conclusión porque la conclusión está contenida en las premisas; por ello varios filósofos han negado la posibilidad de obtener conocimientos a través de la deducción o del razonamiento deductivo, dando lugar a un problema que se conoce como paradoja de la inferencia. Pare enfrentar este problema propongo distinguir entre el razonamiento deductivo como proceso cognitivo capaz de generar conocimientos nuevos y la implicación deductiva como una relación lógica entre proposiciones que da cuenta de la validez de la deducción, y presento evidencia científica que apoya esta propuesta.

El desarrollo de las ciencias sociales enfrenta retos complejos que incluyen elegir la metodología y desarrollo teórico adecuados para abordar sus objetos de estudio. Para contribuir a enfrentar estos retos, se toma como punto de partida el amplio reconocimiento de que un criterio para la aceptación o rechazo de teorías consiste en tomar en cuenta su capacidad para dar cuenta de los fenómenos sociales. Este trabajo se propone mostrar que el desarrollo de teorías que nos permitan comprender los fenómenos sociales requiere de la incorporación de métodos deductivos. La incorporación de tales métodos permitirá fortalecer las teorías en las ciencias sociales y aportará herramientas para resolver algunos de sus problemas más apremiantes. Para ello, se muestra cómo incorporar derivaciones deductivas puede contribuir a solventar la crisis de la falta de desarrollo teórico en ciencias sociales. A continuación, se presentan argumentos desde la filosofía de la ciencia que muestran cómo la incorporación de métodos y relaciones deductivas en las teorías contribuye a su fortalecimiento. Reconociendo la necesidad de complementar estos métodos con enfoques epistemológicos pluralistas, se responde a varias críticas dirigidas al uso de métodos deductivos en las ciencias empíricas en general y en las ciencias sociales en particular.

Accounting for the epistemic contribution of deduction has been a pervasive problem for logicians interested in deduction, such as, among others, Jakko Hintikka. The problem arises because the conclusion validly deduced from a set of premises is said to be “contained” in that set; because of this containment relation, the conclusion would be known from the moment the premises are known. Assuming this, it is problematic to explain how we can gain knowledge by deducing a logical consequence implied by a set of known premises. To address this problem, we offer an alternative account of the epistemic contribution of deduction as the process required to deduce a conclusion or a theorem, understanding such a process not only in terms of the number of steps in the derivation but also, more importantly, in terms of the reason for or justification for every step. That is, we do not know a proposition unless we have a justification or proof of that proposition. With this goal in mind, we develop a justification logic system which exhibits the epistemic contribution of a deductive derivation as the resulting justified formula.