Philosophical Inquiry into Computer Intentionality: Machine Learning and Value Sensitive Design

Intelligent algorithms together with various machine learning techniques hold a dominant position among major challenges for contemporary value sensitive design. Self-learning capabilities of current AI applications blur the causal link between programmer and computer behavior. This creates a vital...

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Bibliographic Details
Published in:	Human affairs (Bratislava, Slovakia) Slovakia), 2023-02, Vol.33 (1), p.115-127
Main Author:	Mykhailov, Dmytro
Format:	Article
Language:	eng
Subjects:	algorithmic transparency Algorithms Artificial intelligence Causality computer intentionality Design Digital technology fairness Intentionality Learning Machine learning machine learning (ML) values sensitive design
Online Access:	Get full text
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Summary:	Intelligent algorithms together with various machine learning techniques hold a dominant position among major challenges for contemporary value sensitive design. Self-learning capabilities of current AI applications blur the causal link between programmer and computer behavior. This creates a vital challenge for the design, development and implementation of digital technologies nowadays. This paper seeks to provide an account of this challenge. The main question that shapes the current analysis is the following: What conceptual tools can be developed within the value sensitive design school of thought for evaluating machine learning algorithms where the causal relation between designers and the behavior of their computer systems has been eroded? The answer to this question will be provided through two levels of investigation within the value sensitive design methodology. The first level is conceptual. Within the conceptual level, we will introduce the notion of computer intentionality and will show how this term may be used for solving an issue of non-causal relation between designer and computer system. The second level of investigation is technical. At this level the emphasis will be given to machine learning algorithms.
ISSN:	1210-3055 1337-401X