Huan Liu

Lu Cheng

In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. In response, researchers and organizations have been working to publish principles and develop AI regulations for the responsible use of AI in consequential application domains. However, these theoretically formulated principles and regulations also need to be turned into actionable algorithms to materialize AI for good.This book introduces a unified perspective of Socially Responsible AI to help bridge conceptual AI principles to responsible AI practice. It begins with an interdisciplinary definition of socially responsible AI and the AI responsibility pyramid. Existing efforts seeking to materialize the mainstream responsible AI principles are then presented. The book also discusses how to leverage advanced AI techniques to address the challenging societal issues through Protecting, Informing, and Preventing, and concludes with open problems and challenges.This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges of socially responsible AI, and to identify how their areas of expertise can contribute to making AI socially responsible.

Socially Responsible Ai: Theories And Practices

Luigi Cerulo

Francesco Napolitano

Francesco Bardozzo

Annalisa Occhipinti

Stefano M. Pagnotta

This volume LNCS 15276 constitutes the revised selected papers of the 19th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2024, held in Benevento, Italy, during September 4–6, 2024. The 24 full papers and 3 short papers were carefully reviewed and selected from 28 submissions. They were organized in the following topical sections: Bioinformatics; Medical Informatics; Natural Language Processing (NLP) and Large Language Models (LLM) for Unstructured Data in Health Informatics; Modeling and Simulation Methods for Computational Biology and Systems Medicine; Machine Learning for Structured Data in Clinical Informatics and Medical Biology; Computational Intelligence in Personalized Medicine; and Computational Structural Bioinformatics.

19th International Meeting, CIBB 2024, Benevento, Italy, September 4–6, 2024, Revised Selected Papers

Computational Intelligence Methods for Bioinformatics and Biostatistics

Eisenstein Series and Rationality of Automorphic L-functions on Anisotropic Unitary Groups Over Function Fields

Lu Cheng

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