Titlebook: Causality for Artificial Intelligence; From a Philosophical Jordi Vallverdú Book 2024 The Editor(s) (if applicable) and The Author(s), unde

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https://doi.org/10.1007/978-1-4612-1318-5al relationships, it explores real-world applications, ethical nuances, and emphasizes robustness, interdisciplinary collaboration, explainability, transparency, and a harmonious human–AI partnership in shaping causal AI’s future. Beginning with successful ventures, the chapter highlights achievemen

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Carlos Pérez-Galván,I. David L. Bogleess, and evaluating system performance. This chapter delves into the application of counterfactuals across various AI domains such as explainable AI, causal inference, reinforcement learning, fairness and bias, and natural language processing. It presents detailed examples, illustrated with pseudoco

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Advances in Stromatolite Geobiologyluence the past. Explored in the context of quantum entanglement, delayed-choice experiments, Wheeler–Feynman Absorber Theory, and the transactional interpretation of quantum mechanics, retrocausality remains speculative. In the realm of machine learning, the chapter explores the implications of ret

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https://doi.org/10.1007/978-3-642-10415-2Drawing parallels with Richard III’s desperate cry for a horse, the chapter underscores the pivotal role causal algorithms play in diverse fields, including healthcare, finance, and policy. The challenges in developing effective causal algorithms mirror the complexities faced by regression models. T

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978-981-97-3189-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor