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Interacting with an inferred world: The challenge of machine learning for humane computer interaction


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Authors

Blackwell, AF 

Abstract

jats:p<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Classic theories of user interaction have been framed in relation to symbolic models of planning and problem solving, responding in part to the cognitive theories associated with AI research. However, the behavior of modern machine-learning systems is determined by statistical models of the world rather than explicit symbolic descriptions. Users increasingly interact with the world and with others in ways that are mediated by such models. This paper explores the way in which this new generation of technology raises fresh challenges for the critical evaluation of interactive systems. It closes with some proposed measures for the design of inference-based systems that are more open to humane design and use. </span></p></div></div></div></jats:p>

Description

Keywords

machine learning, critical theory

Journal Title

Critical Alternatives - Proceedings of the 5th Decennial Aarhus Conference, CA 2015

Conference Name

Journal ISSN

2445-7221
2445-7221

Volume Title

Publisher

Det Kgl. Bibliotek/Royal Danish Library