[SydPhil] CFP: [Philosophy & Technology] Machine Learning and Society: Philosophical and Sociological Perspectives

[Mirko Farina]

Call for Papers for Philosophy & Technology’s special issue on:
'Machine Learning and Society: Philosophical and Sociological
Perspectives'
[https://protect-au.mimecast.com/s/cpjvCXLW2mU4qM201I60odI?domain=link.springer.com]

Guest editors: Mirko Farina (Innopolis:
https://protect-au.mimecast.com/s/6PiiCYW8NocDN6MOQhGQ2_W?domain=mirkofarina.weebly.com and Witold Pedrycz (Alberta:
https://protect-au.mimecast.com/s/y9QXCZY1Nqi7Dol2EcKZLSF?domain=scopus.com

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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.
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To submit a paper for this special issue, please follow the
instructions on the journal's website:
--> https://protect-au.mimecast.com/s/cpjvCXLW2mU4qM201I60odI?domain=link.springer.com

The deadline is 31st of July 2023.

Thank you

With best wishes,
Mirko & Witold
-- 
Mirko Farina, PhD, MPhil
https://protect-au.mimecast.com/s/JpbRC1WLPxc6kE351cX3NNQ?domain=mirkofarina.weebly.com