Dr Anjana Wijekoon a.wijekoon1@rgu.ac.uk
Research Fellow B
Dr Anjana Wijekoon a.wijekoon1@rgu.ac.uk
Research Fellow B
Professor Nirmalie Wiratunga n.wiratunga@rgu.ac.uk
Associate Dean for Research
Dr Ikechukwu Nkisi-Orji i.nkisi-orji@rgu.ac.uk
Chancellor's Fellow
Dr Kyle Martin k.martin3@rgu.ac.uk
Lecturer
Mr Chamath Palihawadana c.palihawadana@rgu.ac.uk
Research Assistant
Dr David Corsar d.corsar1@rgu.ac.uk
Senior Lecturer
Dr Kyle Martin k.martin3@rgu.ac.uk
Editor
Professor Nirmalie Wiratunga n.wiratunga@rgu.ac.uk
Editor
Dr Anjana Wijekoon a.wijekoon1@rgu.ac.uk
Editor
Counterfactual explanations focus on “actionable knowledge” to help end-users understand how a machine learning outcome could be changed to one that is more desirable. For this purpose a counterfactual explainer needs to be able to reason with similarity knowledge in order to discover input dependencies that relate to outcome changes. Identifying the minimum subset of feature changes to action a change in the decision is an interesting challenge for counterfactual explainers. In this paper we show how feature relevance based explainers (such as LIME), can be combined with a counterfactual explainer to identify the minimum sub-set of “actionable features”. We demonstrate our hybrid approach on a real-world use case on student outcome prediction using data from the Campus Moodle Virtual Learning environment. Our preliminary results demonstrate that counterfactual feature weighting to be a viable strategy that should be adopted to minimise the number of actionable changes.
WIJEKOON, A., WIRATUNGA, N., NKILSI-ORJI, I., MARTIN, K., PALIHAWADANA, C. and CORSAR, D. 2021. Counterfactual explanations for student outcome prediction with Moodle footprints. In Martin, K., Wiratunga, N. and Wijekoon, A. (eds.) SICSA XAI workshop 2021: proceedings of 2021 SICSA (Scottish Informatics and Computer Science Alliance) eXplainable artificial intelligence workshop (SICSA XAI 2021), 1st June 2021, [virtual conference]. CEUR workshop proceedings, 2894. Aachen: CEUR-WS [online], session 1, pages 1-8. Available from: http://ceur-ws.org/Vol-2894/short1.pdf
Conference Name | 2021 SICSA (Scottish Informatics and Computer Science Alliance) eXplainable artificial intelligence workshop (SICSA XAI 2021) |
---|---|
Conference Location | [virtual conference] |
Start Date | Jun 1, 2021 |
Acceptance Date | May 20, 2021 |
Online Publication Date | Jun 1, 2021 |
Publication Date | Jul 2, 2021 |
Deposit Date | Jul 29, 2021 |
Publicly Available Date | Jul 29, 2021 |
Publisher | CEUR Workshop Proceedings |
Pages | 1-8 |
Series Title | CEUR workshop proceedings |
Series Number | 2894 |
Series ISSN | 1613-0073 |
Book Title | SICSA XAI workshop 2021: proceedings of 2021 SICSA (Scottish Informatics and Computer Science Alliance) eXplainable artificial intelligence workshop (SICSA XAI 2021) |
Keywords | Explainable; AI; Counterfactual; LIME |
Public URL | https://rgu-repository.worktribe.com/output/1395861 |
Publisher URL | http://ceur-ws.org/Vol-2894/ |
WIJEKOON 2021 Counterfactual explanations
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Copyright Statement
© 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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