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Author: search.filters.author.Martinez-Villaseñor, LourdesStart date: 2024

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    MICAI and the Making of AI in Mexico Through 25 Years of Data-Driven Insight
    (Springer Nature Switzerland, 2025-10-20)
    Edgar Avalos-Gauna
    ;
    Leon Palafox
    ;
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    Risk Factors for Hypertension and Health Policy
    (Springer Nature Switzerland, 2025-10-20)
    Maximino Navarro Mentado
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    ;
    Vladimir Salazar Altamirano
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    Real-Time Emotion Recognition in Intelligent Tutoring Systems
    (Springer Nature Switzerland, 2025-10-20)
    Alcauter, Iara
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    Preface
    (2025)
    ;
    Vazquez Roberto A
    ;
    Ochoa-ruiz Gilberto
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    Human-Friendly Explanation Model Based on the Aristotelian Practical Syllogism for Reinforcement Learning Agents in Urban Intelligent Design
    (Springer Nature Switzerland, 2025)
    Contreras Olivas Daniel Adrian
    ;
    ;
    Alan Crespo Murillo
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    Human-Friendly Explanations Checklist for Reinforcement Learning: XRL H-F-E Checklist
    (Springer Nature Switzerland, 2025)
    Contreras Olivas, Daniel Adrian
    ;
    Explainable Reinforcement Learning (XRL) has emerged as a critical subfield at the intersection of reinforcement learning (RL) and Explainable Artificial Intelligence (XAI), aiming to render the decision-making processes of learning agents interpretable, transparent, and accessible to human users. This paper introduces a comprehensive evaluation framework, the XRL H-F-E Metrics, to assess the human-friendliness of explanations generated by XRL systems. Drawing from interdisciplinary literature in computer science, cognitive psychology, philosophy of science, and human-computer interaction, the framework is structured across four dimensions: foundational principles (e.g., correctness, robustness, bias mitigation), cognitively aligned explanation types (e.g., “why”, “why not”, counterfactuals), characteristics of “good” explanations (e.g., contrastiveness, selectivity, causality), and human-friendly presentation attributes (e.g., comprehensibility, interactivity, personalization). This checklist provides both a theoretical model and a practical tool for fostering transparency and trust in RL applications, while also identifying key directions for future research, including quantitative metrics, adaptive explanations, and emotionally responsive interfaces. ©The authors ©Springer.
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    Preface : Artificial Intelligence – COMIA 2025 17th Mexican Congress, Mexico City, Mexico, May 12–16, 2025, Proceedings, Part I
    (Springer Science and Business Media Deutschland GmbH, 2025)
    ;
    Martínez-Seis, Bella
    ;
    Pichardo-Lagunas, Obdulia
    The Mexican Conference on Artificial Intelligence (COMIA) is an annual academic event organized by the Mexican Society for Artificial Intelligence (SMIA, Sociedad Mexicana de Inteligencia Artificial) since 2008. COMIA serves as the main national forum in the field of artificial intelligence (AI) and represents a key meeting point for the country’s academic and scientific community dedicated to AI. This year, COMIA 2025 was organized by the SMIA in collaboration with Universidad Panamericana; and was held from May 12 to 16, 2025, in Mexico City, Mexico.© The author ©Springer.
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    Ethical Considerations in the Development of Recommendation Systems: Boutique Travel Agency
    (Springer Nature Switzerland, 2025)
    Alćazar-Pérez, Rodolfo
    ;
    This paper explores the ethical implications of implementing a recommendation system within a boutique travel agency specialized in personalized group travel experiences. It covers legal frameworks, internal policies, risk-benefit analyses, and ethical design principles to ensure fair, transparent, and privacy-respecting use of AI. ©The authors ©Springer.
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    A Novel Ethical Design Framework Applied to Image Classification Challenges in the Fashion Industry
    (Springer Nature Switzerland, 2025)
    Guillen Alvarez, Luis
    ;
    ;
    As artificial intelligence (AI) continues to play a pivotal role in image classification applications, the ethical implications of these technologies become increasingly significant. This paper explores the intersection of AI and ethics in the context of image classification, specifically focusing on the application of ethical design principles through a framework for a use of case in the fashion industry involving bags images and social media. This work delves into the integration of a comprehensive ethical framework around all the design process. The case study involves the development and implementation of a neural network tailored for bag image classification, leveraging transfer learning techniques. Through a meticulous examination of the ethical dimensions inherent in image classification, the study aims to establish a foundation for responsible and transparent AI practices. ©The authors ©Springer.