Debates about learning algorithms quickly get on one of two tracks: Either machine learning is regarded as a statistical procedure for data analysis, then questions arise as to the traceability and reliability of these procedures. Or machine learning is placed in the context of artificial intelligence, then questions arise as to the appropriateness of the claims and visions associated with it. The link between both debates, however, lies in the fact that learning machines affect sociality: from the concrete interaction between humans and machines to the learning machine inscribed designs of society, learning algorithms affect the social space. The conference aims to explore these social forms by bringing together philosophers, sociologists, historians, computer scientists, mathematicians, and users of learning algorithms. The society of learning algorithms is to be reconstructed in the algorithmic models as well as in its political visions:
(1) How does the interaction between learning people and learning machines take place?
(2) How does the interaction between learning machines and (learning) machines take place?
(3) What political designs does the society of learning algorithms bring with it?
Interested computer scientists, philosophers, sociologists, historians, mathematicians, and users of learning algorithms can submit contributions on the following topics (non-exclusive):
- Does the interaction between people and learning machines change and if so, how?
- Is there a double contingency - or how do people learn to deal with learning machines?
- Tool, instrument or system: What challenges do learning algorithms pose to technical philosophy?
- Equalization or otherness: How should learning algorithms be socially designed - as human-like or emphatically as different?
- Justice and social identity: How do personal and algorithmic bias relate to each other?
- What are the feedback effects between learning algorithms and society?
- Which assumptions about social forms can be found in algorithms?
- Comprehensibility, decision and practical reason: What kinds of reasons can learning algorithms give us? And what do we need to make reasonable decisions?
Abstracts (max. 3,000 characters including spaces without references) can be submitted until 15th of July 2019. Submissions should be prepared for anonymous review (no information identifying the author). Applicants will be notified latest by 30th of July. Accepted papers will be published in a proceeding volume by Springer.
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