# Ontology drift is a challenge for explainable data governance

arXiv: https://arxiv.org/abs/2108.05401 · HTML: https://mlsanity.com/paper/2108.05401/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 11 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Fair lending needs explainable models for responsible recommendation](https://mlsanity.com/paper/1809.04684/) (2018)
- [Towards Self-Regulating AI: Challenges and Opportunities of AI Model Governance in Financial Services](https://mlsanity.com/paper/2010.04827/) (2020)
- [Paying down metadata debt: learning the representation of concepts using topic models](https://mlsanity.com/paper/2010.04836/) (2020)

## Abstract (arXiv)

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.