Collection
Machine Learning and Society: Philosophical and Sociological Perspectives
Machine learning (ML) is a branch of Artificial Intelligence that focuses on using data and algorithms to mimic the way humans learn. ML has the potential to deeply transform our societies and our economies. As the OECD recently reported: ‘it promises to generate productivity, gains, improve well-being and help address global challenges... Yet, as [its] applications are adopted around the world, their use can raise questions and challenges related to human values, fairness, human determination, privacy, safety, and accountability...’
This topical collection sets out to explore the broad applications of ML in Society. The objective of this collection is therefore to take our readers on a fascinating voyage of recent machine learning advancements, highlighting the systematic changes in algorithms, techniques and methodologies underwent to date but also aptly reflecting on the philosophical, sociological, as well as ethical consequences, overall impact, and general desirability that such widespread adoption may entail for future societies and individuals living within them.
We plan to organise our topical collection around four -basic- thematic (and strongly multidisciplinary) sections, as follows:
- PART A [Machine Learning: a primer]
- PART B [Machine Learning in Policy Making]
- PART C [Machine Learning in Society]
- PART D [The Future World of Machine Learning]
PART A provides a primer on the algorithms, techniques, and statistical methods used by computer scientists in machine learning. PART B broadly assesses -from the perspective of general policy making- the conditions for the application of ML in society (ideally, in fields such as government and management, education, healthcare, and environmental protection). PART C reviews and evaluates the merits, possibilities, and challenges associated to the widespread implementations of ML in ‘lived environments’ (ideally, in fields such as internet of things, automated transportation, industrial automation, and hiring procedures). Finally, PART D offers a series of careful reflections on major ethical and privacy issues (ranging from algorithmic transparency, accountability, and fairness to responsibility, interpretability, and bio-security).
Editors
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Mirko Farina
Mirko Farina is Professor (Senior Researcher) and Head of the Human-Machine Interaction Lab at the Institute for Digital Economy and Artificial Systems [IDEAS] established -under the framework of the 'BRICS Partnership on New Industrial Revolution'- in Xiamen (People's Republic of China), by Xiamen University [XMU], Lomonosov Moscow State University [MSU] and Xiamen Municipal People's Government. Prior to that he was an Associate Professor of Philosophy of Computer Science at Innopolis University.
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Witold Pedrycz
Witold Pedrycz is Professor in the Dept of Electrical and Computer Engineering at the University of Alberta, Canada. He is also affiliated with the Systems Research Institute of the Polish Academy of Sciences. He is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. His main research directions involve Computational Intelligence, Granular Computing, and Machine Learning, among others. He serves as an EiC of Information Sciences, EiC of WIREs Data Mining and Knowledge Discovery, and Co-EiC of J. of Data Information and Management.
