Given the pervasiveness of AI systems and their potential negative effects on people’s lives (especially among already marginalised groups), it becomes imperative to comprehend what goes on when an AI system generates a result, and based on what reasons, it is achieved. There are consistent technical efforts for making systems more “explainable” by reducing their opaqueness and increasing their interpretability and explainability. In this paper, we explore an alternative non-technical approach towards explainability that complement existing ones. Leaving aside technical, statistical, (...) or data-related issues, we focus on the very conceptual underpinnings of the design decisions made by developers and other stakeholders during the lifecycle of a machine learning project. For instance, the design and development of an app to track snoring to detect possible health risks presuppose some picture or another of “health”, which is a key notion that conceptually underpins the project. We take it as a premise that these key concepts are necessarily present during design and development, albeit perhaps tacitly. We argue that by providing “justificatory explanations” about how the team understands the relevant key concepts behind its design decisions, interested parties could gain valuable insights and make better sense of the workings and outcomes of systems. Using the concept of “health”, we illustrate how a particular understanding of it might influence decisions during the design and development stages of a machine learning project, and how making this explicit by incorporating it into ex-post explanations might increase the explanatory and justificatory power of these explanations. We posit that a greater conceptual awareness of the key concepts that underpin design and development decisions may be beneficial to any attempt to develop explainability methods. We recommend that “justificatory explanations” are provided as technical documentation. These are declarative statements that contain at its simplest: (1) a high-level account of the understanding of the relevant key concepts a team possess related to a project’s main domain, (2) how these understandings drive decision-making during the life-cycle stages, and (3) it gives reasons (which could be implicit in the account) that the person or persons doing the explanation consider to have plausible justificatory power for the decisions that were made during the project. (shrink)

To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. ​Studies mentioning biases on AI algorithms developed for contact (...) tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socio-economic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps. (shrink)

This paper attempts to show how the “big data” paradigm is changing science through offering access to millions of database elements in real time and the computational power to rapidly process those data in ways that are not initially obvious. In order to gain a proper understanding of these changes and their implications, we propose applying an extended cognition model to the novel scenario.

The main aim of this article is to reflect on the impact of biases related to artificial intelligence (AI) systems developed to tackle issues arising from the COVID-19 pandemic, with special focus on those developed for triage and risk prediction. A secondary aim is to review assessment tools that have been developed to prevent biases in AI systems. In addition, we provide a conceptual clarification for some terms related to biases in this particular context. We focus mainly on nonracial biases (...) that may be less considered when addressing biases in AI systems in the existing literature. In the manuscript, we found that the existence of bias in AI systems used for COVID-19 can result in algorithmic justice and that the legal frameworks and strategies developed to prevent the apparition of bias have failed to adequately consider social determinants of health. Finally, we make some recommendations on how to include more diverse professional profiles in order to develop AI systems that increase the epistemic diversity needed to tackle AI biases during the COVID-19 pandemic and beyond. (shrink)

La transición digital de nuestro tiempo, favorecida por los avances de la inteligencia artificial, evidencia retos complejos en las sociedades digitales emergentes. Las artes plásticas se han visto influenciadas por esta digitalización, tanto en lo relativo a los procesos creativos como en lo que concierne al acceso y la exposición de las obras artísticas. En este trabajo se presenta un análisis filosófico centrado en el patrimonio, a saber, su almacenamiento, representación y exposición digital de las obras artísticas. Concretamente, nos servimos (...) de la teoría de injusticia epistémica de Miranda Fricker para un estudio ético y epistemológico sobre el uso de las bases de datos en el diseño de los museos virtuales, insistiendo en el papel de estas para la perpetuación o mitigación de las injusticias que pueden darse en este contexto. Finalmente, se propone la abducción como punto de partida para un análisis filosófico futuro más amplio sobre el museo digital, donde se consideren las relaciones entre visitantes y museos y su influencia en la distribución epistémico-moral en estos entornos. (shrink)

Presentation of the section on Artificial Intelligence, Data and Objectivity: The Return of Data Naturalism? in the Monograph on Artificial Intelligence of Daimon - International Journal of Philosophy. Nº 90 (September - December 2023). Presentación de la sección sobre Inteligencia artificial, datos y objetividad. ¿El regreso del naturalismo dataista? en el Monográfico sobre Inteligencia Artificial de Daimon - Revista Internacional de Filosofía. Nº 90 (septiembre - Diciembre 2023).

Este artículo parte del debate en filosofía de la ciencia entre la tradición teórica y la experimental, y muestra su relación con el estudio de los procesos de innovación e invención en ciencia, cruzando así los planteamientos de análisis más teóricos de la filosofía de la ciencia con cuestiones más relacionadas con la filosofía de la tecnología y la ciencia aplicada. De esta manera, analizamos la interrelación entre experimento y teoría en los procesos de invención e innovación y conectamos los (...) campos de la ciencia teórica y la aplicada, mostrando la continuidad entre ambas. Así, podemos mostrar también cómo en ciencia hay siempre dependencia mutua de teoría y experimentación, y cómo esa dependencia es extrapolable también a los procesos de innovación e invención.Tomando como punto de partida el debate en torno a las tradiciones teóricas y experimentales, veremos hasta qué punto los argumentos que cuestionan las tradiciones teóricas y apuestan por las tradiciones experimentales encajan con los fenómenos de invención e innovación. El caso que vamos a tomar como referencia para aplicar este análisis es el de «aprendizaje automático», como una rama de los algoritmos computacionales diseñados para emular la inteligencia humana aprendiendo del entorno. Este campo es relevante pues, a pesar de su naturaleza eminentemente teórica —en substancia es matemática aplicada—, presenta toda una serie de características que lo hacen muy afín al análisis desde las tradiciones experimentales. (shrink)

As AI is linked to more and more aspects of our lives, the need for algorithms that can take decisions that are not only accurate but also fair becomes apparent. It can be seen both in discussions of future trends such as autonomous vehicles or the issue of superintelligence, as well as actual implementations of machine learning used to decide whether a person should be admitted in certain university or will be able to return a credit. In this paper, we (...) will use Dreyfus’ account on ethical expertise to show that, to give an AI some ability to make ethical judgements, a pure symbolic, conceptual approach is not enough. We also need the ability to make sense of the surroundings to reframe and define situations in a dynamic way, using multiple perspectives in a pre-reflective way. (shrink)

El objetivo de este artículo es analizar y revisar las normas que filosóficamente asociamos al proceso de testimonio, inquiriendo hasta qué puntoson0 consistentes con los conocimientos empíricos de las ciencias cognitivas.Tradicionalmente, el problema del testimonio surgía cuando, desde una epistemología de corte individualista, se suponía, siguiendo el dictum ya marcado en la Modernidad tanto por racionalistas como por empiristas, de que el conocimiento debía ser testado personalmente. Sin embargo, disciplinas y enfoques recientes, como la Cognición Socialmente Distribuida y la Epistemología (...) Social ofrecen otra vía al considerar al grupo la unidad cognitiva realmente significativa e intentar solucionar el problema del testimonio desde esa perspectiva. Nuestro objetivo es analizar los motivos por los que la CSD es un buen modelo para explicar algunas de las paradojas y problemas epistemológicos cuando tenemos en cuenta el papel del testimonio en el desarrollo de la ciencia. The goal of this paper is to review and analyze norms philosophically associated with the process of testimony and to investigate to what extent they are consistent with empirical knowledge supplied by cognitive science. Traditionally, the problem of testimony used to arise when it was supposed, from the viewpoint of an individualistic epistemology, that followed the dictum stated by rationalists and empiricists in Modernity, that knowledge had to be tested personally. However, recent disciplines and approaches, like Socially Distributed Cognition and Social Epistemology, provide alternative ways of thinking. This is the case when we consider the group as the truly significant cognitive unit and from such perspective we try to solve the problem of testimony. Therefore, our aim is to examine the reasons why SDC offers a good model for explaining some of the paradoxes and epistemological problems that arise when we consider the issue of testimony in the development of science. (shrink)

The purpose of this work is twofold: (1) to present an artistic experiment on how to use artificial intelligence to develop a “different kind” of DJ, and (2) to test a cognitive model on how music expresses emotions. Based on a former model conceived by the author, electronic music loops were tagged according to the type and intensity of the expressed emotion. Then, using a feedback model, an artificial personality was arranged, which was affected by the music and played the (...) loops depending on the emotional state the artificial personality was in. The efficiency and credibility of the model was tested in a “live event” during the Z2000 art festival in Berlin, with encouraging results. (shrink)