[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127276-en":3,"doc-seo-127276-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127276,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Breaking Away From AI - The Ontological and Ethical Evolution of Machine Learning","Machine Learning (ML) has historically been linked to Artificial Intelligence (AI), yet has matured into a distinct discipline. This paper argues for ML’s ontological independence, supported by its unique methodologies, applications, and ethical requirements. A bibliometric analysis shows ML output (494,572 publications, 2017–2023) exceeding AI (283,762) by 74%, evidencing rapid specialization. The study contrasts AI’s general-intelligence and symbolic ambitions with ML’s data-driven performance focus, highlighting ethics issues like bias, fairness, and environmental sustainability and calling for ML-specific ethical frameworks.","Received 8 February 2025, accepted 11 March 2025, date of publication 19 March 2025, date of current version 4 April 2025. Digital Object Identifier 10.1109/ACCESS.2025.3553032  \nBreaking Away From AI: The Ontological and Ethical Evolution of Machine Learning  \nENRICO BARBIERATO1, ALICE GATTI1, ALESSANDRO INCREMONA1, ANDREA POZZI  1, AND DANIELE TOTI1,2,(Member, IEEE)  \n1Faculty of Mathematical, Physical and Natural Sciences, Catholic University of the Sacred Heart, 25133 Brescia, Italy  \n2Department of Sciences, Roma Tre University, 00146 Rome, Italy  \nCorresponding author: Daniele Toti ([daniele.toti@uniroma3.it](daniele.toti@uniroma3.it))  \nThis work was supported by the European Union–Next Generation EU, Mission 4, Component 2, under Grant Agreement (CUP), under Grant 2022AEEKXS.  \nABSTRACT Machine Learning (ML) has historically been associated with Artificial Intelligence (AI) but has developed into an independent discipline. This paper argues for the ontological independence of ML, driven by its unique methodologies, applications, and ethical considerations. A bibliometric analysis reveals that ML research output (494,572 publications from 2017–2023) surpasses AI (283,762 publications) by 74%, reflecting its rapid growth and specialization. Unlike AI’s pursuit of general intelligence and symbolic reasoning, ML focuses on data-driven performance optimization, with impactful applications in computer vision, natural language processing (NLP), and autonomous systems. The study highlights ethical challenges—such as addressing algorithmic bias (50 occurrences), fairness (2,778 publications), and environmental sustainability (283 related works)—which emphasize the need for dedicated ethical frameworks tailored to ML. These findings propose a conceptual and practical separation between ML and AI to enable targeted research, interdisciplinary collaboration, and solutions to challenges like explainability, transparency, and sustainability. The paper underscores the importance of recognizing ML’s independence in advancing both fields.  \nINDEX TERMS Machine learning (ML), artificial intelligence (AI), deep learning (DL), fairness, bias, epistemology, ethical artificial intelligence, explainability, bibliometric analysis, sustainability, natural language processing (NLP), computer vision.  \nI. INTRODUCTION  \nScientific disciplines occasionally intersect, sharing common objectives, methodologies, or foundational principles. This overlap arises when fields engage with similar phenomena, employ comparable research techniques, or are considered part of a broader scientific paradigm. For example, biology and chemistry share experimental methods and occasionally intersect when investigating molecular processes, yet each field remains distinct in its focus: biology centers on living organisms, while chemistry emphasizes matter’s composition and structure. Similarly, physics and astronomy both study the universe, though physics prioritizes fundamental laws, and astronomy explores cosmic structures and phenomena [1] . In historical contexts, the classification of knowledge  \nThe associate editor coordinating the review of this manuscript and  \napproving it for publication was Loris Belcastro  .  \nhas often shifted to accommodate emerging fields. The term‘‘Natural Philosophy,’’ attributed to Aristotle [2], served asan early descriptor for a collection of studies, including physics and astronomy, until the rise of formalized scientific disciplines. During the Middle Ages, theology utilized mathematics and astronomy to support its frameworks, demonstrating the interconnected nature of knowledge atthe time. Later, intellectual developments contributed to forming independent fields like physics, which gained its status as a distinct discipline in the 19th century through focused methodologies and objectives [3] . The process of distinguishing a field as an ontologically independent science is nuanced. Chemistry, for example, relies on physical pri","cbCainbNzx8gE5xg","https://ap.wps.com/l/cbCainbNzx8gE5xg","pdf",2058101,1,21,"English","en",105,"# Introduction\n## Epistemological Distinction Between AI and ML\n## Bibliometric Analysis of Research Output\n## Ethical Challenges in Machine Learning\n## Implications for Explainability, Transparency, and Sustainability","[{\"question\":\"What is the central claim of the paper about machine learning and AI?\",\"answer\":\"The paper argues that machine learning has developed into an ontologically independent field, distinct from AI in epistemology, specialization, and trajectory.\"},{\"question\":\"How does the paper support the claim empirically?\",\"answer\":\"It uses a bibliometric analysis showing ML research output from 2017–2023 (494,572 publications) surpasses AI (283,762) by 74%, indicating rapid growth and specialization.\"},{\"question\":\"What ethical issues does the paper emphasize for machine learning?\",\"answer\":\"The paper highlights algorithmic bias, fairness, and environmental sustainability, and argues for ethical frameworks tailored specifically to ML.\"}]","Breaking Away From AI - 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