{"type":"ScholarlyArticle","title":"The Role of Textualisation and Argumentation in Understanding the Machine Learning Process","authors":[{"name":"Kacper Sokol","slug":"kacper-sokol","given":"Kacper","family":"Sokol","affiliation":"Department of Computer Science , University of Bristol"},{"name":"Peter Flach","slug":"peter-flach","given":"Peter","family":"Flach","affiliation":"Department of Computer Science , University of Bristol"}],"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.","doi":"10.24963/ijcai.2017/765","url":"https://ijcai.org/proceedings/2017/765","pdf":"https://ijcai.org/proceedings/2017/0765.pdf","markdown":"https://ijcai.org/proceedings/2017/765.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/765","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":765,"pages":{"first":5211,"last":5212},"keywords":["Artificial Intelligence: machine learning","Artificial Intelligence: artificial intelligence","Artificial Intelligence: knowledge representation","Artificial Intelligence: natural language processing"],"videos":[],"references":[{"position":1,"raw":"[Farrell et al., 2015] Rachel Farrell, Gordon Pace, and Michael Rosner. A framework for the generation of com- puter system diagnostics in natural language using finite state methods. In Proceedings of the 15th European Work- shop on Natural Language Generation, 52-56, 2015.","title":"A framework for the generation of computer system diagnostics in natural language using finite state methods","authors":"Rachel Farrell, Gordon Pace, Michael Rosner","year":"2015","venue":"Proceedings of the 15th European Workshop on Natural Language Generation","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Maaten and Hinton, 2008] Laurens van der Maaten and Ge- offrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(Nov):2579-2605, 2008.","title":"Laurens van der Maaten and Geoffrey Hinton","authors":"Hinton Maaten","year":"2008","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Možina et al., 2007] Martin Možina, Jure Žabkar, and Ivan Bratko. Argument based machine learning. Artificial In- telligence, 171(10-15):922-937, 2007.","title":"Argument based machine learning","authors":"Martin Možina, Jure Žabkar, Ivan Bratko","year":"2007","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Portet et al., 2009] Franc ¸ois Portet, Ehud Reiter, Albert Gatt, Jim Hunter, Somayajulu Sripada, Yvonne Freer, and Cindy Sykes. Automatic generation of textual summaries from neonatal intensive care data. Artificial Intelligence, 173(7):789-816, 2009.","title":"Automatic generation of textual summaries from neonatal intensive care data","authors":"Portet","year":"2009","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}