Fifty Shades of Grey:In Praise of a Nuanced Approach Towards Trustworthy Design

Thornton, Lauren and Knowles, Bran and Blair, Gordon (2021) Fifty Shades of Grey:In Praise of a Nuanced Approach Towards Trustworthy Design. In: ACM FAccT’21. ACM, New York, pp. 64-76. ISBN 9781450383097

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Abstract

Environmental data science is uniquely placed to respond to essentially complex and fantastically worthy challenges related to arresting planetary destruction. Trust is needed for facilitating collaboration between scientists who may share datasets and algorithms, and for crafting appropriate science-based policies. Achieving this trust is particularly challenging because of the numerous complexities, multi-scale variables, interdependencies and multi-level uncertainties inherent in environmental data science. Virtual Labs---easily accessible online environments provisioning access to datasets, analysis and visualisations---are socio-technical systems which, if carefully designed, might address these challenges and promote trust in a variety of ways. In addition to various system properties that can be utilised in support of effective collaboration, certain features which are commonly seen to benefit trust---transparency and provenance in particular---appear applicable to promoting trust in and through Virtual Labs. Attempting to realise these features in their design reveals, however, that their implementation is more nuanced and complex than it would appear. Using the lens of affordances, we argue for the need to carefully articulate these features, with consideration of multiple stakeholder needs on balance, so that these Virtual Labs do in fact promote trust. We argue that these features not be conceived as widgets that can be imported into a given context to promote trust; rather, whether they promote trust is a function of how systematically designers consider various (potentially conflicting) stakeholder trust needs.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency March 2021 Pages 64–76 https://doi.org/10.1145/3442188.3445871
ID Code:
150106
Deposited By:
Deposited On:
18 Dec 2020 15:30
Refereed?:
Yes
Published?:
Published
Last Modified:
17 Oct 2021 00:37