The Role of Textualisation and Argumentation in Understanding the Machine Learning Process
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.
Cite
Text
Sokol and Flach. "The Role of Textualisation and Argumentation in Understanding the Machine Learning Process." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/765Markdown
[Sokol and Flach. "The Role of Textualisation and Argumentation in Understanding the Machine Learning Process." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/sokol2017ijcai-role/) doi:10.24963/IJCAI.2017/765BibTeX
@inproceedings{sokol2017ijcai-role,
title = {{The Role of Textualisation and Argumentation in Understanding the Machine Learning Process}},
author = {Sokol, Kacper and Flach, Peter A.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {5211-5212},
doi = {10.24963/IJCAI.2017/765},
url = {https://mlanthology.org/ijcai/2017/sokol2017ijcai-role/}
}