• Corpus ID: 236986933

Ontology drift is a challenge for explainable data governance

@article{Chen2021OntologyDI,
  title={Ontology drift is a challenge for explainable data governance},
  author={Jiahao Chen},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.05401}
}
  • Jiahao Chen
  • Published 11 August 2021
  • Computer Science
  • 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, explainable AI 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… 

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References

SHOWING 1-10 OF 22 REFERENCES
The financial industry business ontology: Best practice for big data
This article describes the Financial Industry Business Ontology (FIBO) as a set of formal models that define unambiguous shared meaning for financial industry concepts. An account is given of the
Designing data governance
TLDR
An overall framework for data governance is provided that can be used by researchers to focus on important data governance issues, and by practitioners to develop an effective data governance approach, strategy and design.
Concept drift and how to identify it
TLDR
A qualitative toolkit for analysing concept drift is proposed to detect concept shift and stability when concept identity is available, and concept split and strength of morphing chain if using the morphing theory.
Towards self-regulating AI: challenges and opportunities of AI model governance in financial services
TLDR
This paper presents a system-level framework towards increased self-regulation for robustness and compliance and aims to enable potential solution opportunities through increased automation and the integration of monitoring, management, and mitigation capabilities.
Fair lending needs explainable models for responsible recommendation
The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of
Paying down metadata debt: learning the representation of concepts using topic models
TLDR
A gauge transformation approach is introduced that allows us to construct explicit associations between topics and concept labels, and thus assign meaning to topics, and the ability to learn semantically meaningful features is demonstrated.
Transforming Paradigms: A Global AI in Financial Services Survey
This report presents the findings of a global survey on AI in Financial Services jointly conducted by the Cambridge Centre for Alternative Finance (CCAF) at the University of Cambridge Judge Business
Ad Hoc Monitoring of Vocabulary Shifts over Time
TLDR
The introduction of the task of ad hoc monitoring of vocabulary shifts over time, the description of an algorithm for tracking shifting vocabularies over time given a small set of seed words, and a systematic evaluation of results over a substantial period of time are introduced.
A survey on concept drift adaptation
TLDR
The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art and aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.
North American Industry Classification System
The detailed NAICS structure along with a brief description of the structure is attached (Attachments 1 and 2). Each country agrees to release a copy of the proposed NAICS structure to interested
...
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2
3
...