Semantic Explanations of Predictions
By Freddy Lécué, Jiewen Wu. 2018
11 citations recorded by the source corpus.
The main objective of explanations is to transmit knowledge to humans. This work proposes to construct informative explanations for predictions made from machine learning models. Motivated by the observations from social sciences, our approach selects data points from the training sample that exhibit special characteristics crucial for explanation, for instance, ones contrastive to the classification prediction and ones representative of the models. Subsequently, semantic concepts are derived from the selected data points through the use of domain ontologies. These concepts are filtered and ranked to produce informative explanations that improves human understanding. The main features of our approach are that (1) knowledge about explanations is captured in the form of ontological concepts, (2) explanations include contrastive evidences in addition to normal evidences, and (3) explanations are user relevant.
Subfields: Artificial Intelligence, Information Systems and Management
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Andreas Holzinger
- Katharina Holzinger
- Randy Goebel
- Peter Kieseberg
- Freddy Lécué
- Ajay Chander
- Simone Stumpf
- Zeynep Akata
In Information Systems and Management
Citations in the corpus
- Explainable AI: The New 42? (2018, positive): “, combining logic-based system with classic stochastic systems to derive human-understandable semantic explanations [14].”