From Invisible Algorithms to Interactive Affordances: Data after the Ideology of Machine Learning

E. Bertino, S. A. Matei (eds.), Roles, Trust, and Reputation in Social Media Knowledge 1 Markets, Computational Social Sciences, DOI 10.1007/978-3-319-05467-4_7,
 Forthcoming.

14 Pages Posted: 2 Jul 2014

See all articles by Bernie Hogan

Bernie Hogan

University of Oxford - Oxford Internet Institute

Date Written: November 1, 2013

Abstract

The presentation of online information is dominated by single ranked lists. These lists tend to be sorted by increasingly invisible algorithms (often employing personalization features). Instead of alphabetical or chronological order, information providers use the logic of machine learning to train the system. This ideology encourages less from users and more from data providers. I present examples, primarily from the notion of data-as-graphs to demonstrate alternative approaches to information presentation. I do this in three domains: music, email and friending. I contend that these new approaches open up new ways of thinking about data while also providing significant new technological challenges.

Keywords: algorithms, machine learning, social computing, affordances, HCI

Suggested Citation

Hogan, Bernie, From Invisible Algorithms to Interactive Affordances: Data after the Ideology of Machine Learning (November 1, 2013). E. Bertino, S. A. Matei (eds.), Roles, Trust, and Reputation in Social Media Knowledge 1 Markets, Computational Social Sciences, DOI 10.1007/978-3-319-05467-4_7,
 Forthcoming. . Available at SSRN: https://ssrn.com/abstract=2461243

Bernie Hogan (Contact Author)

University of Oxford - Oxford Internet Institute ( email )

1 St. Giles
University of Oxford
Oxford OX1 3PG Oxfordshire, Oxfordshire OX1 3JS
United Kingdom

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