Explainable artificial intelligence
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This article is an todo. It will be expanded only with confirmed details.
Explainable artificial intelligence is recorded as one of Ko Yamasaki's research interests.
Within this encyclopedia, the topic is connected especially to machine learning over structured data and to the problem of explaining predictions made by graph neural networks.
Background[edit]
Machine learning models can produce accurate predictions while remaining difficult to interpret.
Explainable AI concerns methods for understanding what information contributed to a prediction, how a model used input features, and whether a result can be trusted or communicated to users.
For the main biographical article, see Ko Yamasaki.
Explanations in graphs[edit]
In graph learning, explanation is especially complex because the model may rely not only on node features but also on edges, neighborhoods, and substructures.
For GNNs, an explanation may identify important nodes, edges, paths, or local graph patterns that influenced a prediction.
Relation to Ko Yamasaki[edit]
Yamasaki's interest in XAI is tied to his broader concern with structure. If a model uses relationships among data points, he is interested not only in the output but also in which relationships mattered.
This makes XAI a bridge between Machine learning, graph representation, and the future research direction described in the Research article.
