CfP: 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’ (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). 

All approaches, methodologies, and schools of thought are welcome, with particular attention to sound and evidence-based reasoning.