Abstract
The amount and complexity of generated and collected data is rapidly growing. As a consequence, it is increasingly hard to understand the data and extract useful information. Transparency, interpretability and understandability contribute towards explainability of the data, which is crucial for the user for both efficient and effective usage of it and trust in these data-based decisions. In this paper, we investigate how linguistic summaries can serve as an explanation mechanism for classification results. Linguistic summaries are template-based, semi-natural language-like sentences that can verbalize these (classification) patterns. We develop linguistic summarizations for the classification
results of two publicly available data sets and perform an initial evaluation with a small group of potential users. The preliminary results look promising.
results of two publicly available data sets and perform an initial evaluation with a small group of potential users. The preliminary results look promising.
Original language | English |
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Publication status | Published - Nov 2022 |
Event | The 34th Benelux Conference on Artificial Intelligence and the 31th Belgian Dutch Conference on Machine Learning - Mechelen, Belgium Duration: 7 Nov 2022 → 9 Nov 2022 |
Conference
Conference | The 34th Benelux Conference on Artificial Intelligence and the 31th Belgian Dutch Conference on Machine Learning |
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Abbreviated title | BNAIC 2022 |
Country/Territory | Belgium |
City | Mechelen |
Period | 7/11/22 → 9/11/22 |
Keywords
- Explainable AI
- Linguistic Summaries
- Classification