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The role of textualisation and argumentation in understanding the machine learning process

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Subtitle of host publicationProceedings of a meeting held 19-25 August 2017, Melbourne, Australia.
Publisher or commissioning bodyInternational Joint Conferences on Artificial Intelligence
Pages5211-5212
Number of pages2
ISBN (Electronic)9780999241103
ISBN (Print)9780999241110
DOIs
DateAccepted/In press - 23 Apr 2017
DatePublished (current) - 25 Aug 2017
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017

Conference

Conference26th International Joint Conference on Artificial Intelligence, IJCAI 2017
CountryAustralia
CityMelbourne
Period19/08/1725/08/17

Abstract

Understanding data, models and predictions is important for machine learning applications. Due to the limitations of our spatial perception and intuition, analysing high-dimensional data is inherently difficult. Furthermore, black-box models achieving high predictive accuracy are widely used, yet the logic behind their predictions is often opaque. Use of textualisation - a natural language narrative of selected phenomena - can tackle these shortcomings. When extended with argumentation theory we could envisage machine learning models and predictions arguing persuasively for their choices.

Event

26th International Joint Conference on Artificial Intelligence, IJCAI 2017

Duration19 Aug 201725 Aug 2017
CityMelbourne
CountryAustralia
SponsorsAlibaba Group (External organisation), Artificial Intelligence Journal (External organisation), et al. (External organisation), Melbourne Convention Bureau (External organisation), Victoria Government (External organisation), Xiaoi (External organisation)

Event: Conference

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via IJCAI at https://doi.org/10.24963/ijcai.2017/765 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 75 KB, PDF document

    Licence: Other

DOI

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