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The Need for Sensemaking in Networked Privacy and Algorithmic Responsibility

Van Kleek, M; Seymour, W; Veale, M; Binns, R; Shadbolt, N; (2018) The Need for Sensemaking in Networked Privacy and Algorithmic Responsibility. In: Sensemaking in a Senseless World: Workshop at ACM CHI’18, 22 April 2018, Montréal, Canada. Association for Computing Machinery (ACM) (In press). Green open access

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Abstract

This paper proposes that two significant and emerging problems facing our connected, data-driven society may be more effectively solved by being framed as sensemaking challenges. The first is in empowering individuals to take control of their privacy, in device-rich information environments where personal information is fed transparently to complex networks of information brokers. Although sensemaking is often framed as an analytical activity undertaken by experts, due to the fact that non-specialist end-users are now being forced to make expert-like decisions in complex information environments, we argue that it is both appropriate and important to consider sensemaking challenges in this context. The second is in supporting human-in-the-loop algorithmic decision-making, in which important decisions bringing direct consequences for individuals, or indirect consequences for groups, are made with the support of data-driven algorithmic systems. In both privacy and algorithmic decision-making, framing the problems as sensemaking challenges acknowledges complex and illdefined problem structures, and affords the opportunity to view these activities as both building up relevant expertise schemas over time, and being driven potentially by recognition-primed decision making.

Type: Proceedings paper
Title: The Need for Sensemaking in Networked Privacy and Algorithmic Responsibility
Event: ACM Conference on Human Factors in Computing Systems (CHI'18)
Location: Montréal, Canada
Dates: 21 April 2018 - 27 April 2018
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Sensemaking; end-user privacy; transparency; fair and accountable machine learning
URI: http://discovery.ucl.ac.uk/id/eprint/10046886
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