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Publication - Mr Kacper Sokol

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

    Citation

    Sokol, K & Flach, P, 2017, ‘The role of textualisation and argumentation in understanding the machine learning process’. in: 26th International Joint Conference on Artificial Intelligence, IJCAI 2017: Proceedings of a meeting held 19-25 August 2017, Melbourne, Australia.. International Joint Conferences on Artificial Intelligence, pp. 5211-5212

    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.

    Full details in the University publications repository