Abstract: A method, system, and computer program product generate precomputed explanation scores in AI systems. The method includes obtaining a set of labeled transactions comprising input features and corresponding output labels generated by a machine learning (ML) model and generating an explainable artificial intelligence (XAI) module. The generating includes clustering the labeled transactions based on the input features, scoring homogeneity of the clustered transactions based on the corresponding output labels, and selecting at least one cluster from the clustered transactions based on the homogeneity scoring. The generating further includes obtaining, by an explainability model, explainability scores for transactions in the at least one cluster, generating a unified explainability score for the at least one cluster based on the explainability scores, and storing the unified explainability score in a set of precomputed explanations.

Abstract: A first input transaction is classified into a first input space cluster in a set of input space clusters. It is determined that the first input space cluster maps to a single explainability space cluster in a set of explainability space clusters. Using an interpretable model corresponding to the single explainability space cluster, a first machine learning model prediction is explained, the first machine learning model prediction resulting from processing, by a machine learning model, the first input transaction.

Abstract: In an approach to creating explanatory confusion matrices, responsive to receiving a machine learning model for analysis, a confusion matrix is calculated for the machine learning model, where each cell in the confusion matrix has a corresponding set of data. A link is created from each cell in the confusion matrix to the corresponding set of data. Responsive to a user selecting in a user interface a specific cell of the confusion matrix, the corresponding set of data to the specific cell is displayed on the user interface.

Abstract: Concepts for managing a supervised machine learning model of a set of documents are presented. A system obtains annotated versions of the documents, the documents being annotated by annotators. A conflict between a plurality of annotations of the annotated versions of the documents is identified. The machine learning model includes a set of entities and relations defining relationships between entities. The identified conflict is resolved by at least one of identifying the correct annotation between the conflicting options, splitting the annotated text into two separate entities or relations, generating a new entity at the same or a less specific hierarchical level as the entities or relation in conflict, and/or changing an annotation of the annotated version of the document.

Abstract: Concepts for managing a supervised machine learning model of a set of documents are presented. A system obtains annotated versions of the documents, the documents being annotated by annotators. A conflict between a plurality of annotations of the annotated versions of the documents is identified. The machine learning model includes a set of entities and relations defining relationships between entities. The identified conflict is resolved by at least one of identifying the correct annotation between the conflicting options, splitting the annotated text into two separate entities or relations, generating a new entity at the same or a less specific hierarchical level as the entities or relation in conflict, and/or changing an annotation of the annotated version of the document.