Ontology drift is a challenge for explainable data governance
Computers and Society
2021-08-13 v1 Artificial Intelligence
Information Retrieval
Machine Learning
Abstract
We introduce the needs for explainable AI that arise from Standard No. 239 from the Basel Committee on Banking Standards (BCBS 239), which outlines 11 principles for effective risk data aggregation and risk reporting for financial institutions. Of these, explainableAI is necessary for compliance in two key aspects: data quality, and appropriate reporting for multiple stakeholders. We describe the implementation challenges for one specific regulatory requirement:that of having a complete data taxonomy that is appropriate for firmwide use. The constantly evolving nature of financial ontologies necessitate a continuous updating process to ensure ongoing compliance.
Cite
@article{arxiv.2108.05401,
title = {Ontology drift is a challenge for explainable data governance},
author = {Jiahao Chen},
journal= {arXiv preprint arXiv:2108.05401},
year = {2021}
}
Comments
5 pages, 2 figures, 1 table. Data and code included in source