Law, S;
Hasegawa, R;
Paige, B;
Russell, C;
Elliott, A;
(2023)
Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals.
International Journal of Geographical Information Science
10.1080/13658816.2023.2214592.
(In press).
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Abstract
We propose a new form of plausible counterfactual explanation designed to explain the behaviour of computer vision systems used in urban analytics that make predictions based on properties across the entire image, rather than specific regions of it. We illustrate the merits of our approach by explaining computer vision models used to analyse street imagery, which are now widely used in GeoAI and urban analytics. Such explanations are important in urban analytics as researchers and practioners are increasingly reliant on it for decision making. Finally, we perform a user study that demonstrate our approach can be used by non-expert users, who might not be machine learning experts, to be more confident and to better understand the behaviour of image-based classifiers/regressors for street view analysis. Furthermore, the method can potentially be used as an engagement tool to visualise how public spaces can plausibly look like. The limited realism of the counterfactuals is a concern which we hope to improve in the future.
| Type: | Article |
|---|---|
| Title: | Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/13658816.2023.2214592 |
| Publisher version: | https://doi.org/10.1080/13658816.2023.2214592 |
| Language: | English |
| Additional information: | © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
| Keywords: | Urban analytics; counterfactual explanations; explainable AI; streetview; urban design |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10171373 |
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