English

A taxonomy of explanations to support Explainability-by-Design

Artificial Intelligence 2024-11-15 v2 Computers and Society

Abstract

As automated decision-making solutions are increasingly applied to all aspects of everyday life, capabilities to generate meaningful explanations for a variety of stakeholders (i.e., decision-makers, recipients of decisions, auditors, regulators...) become crucial. In this paper, we present a taxonomy of explanations that was developed as part of a holistic 'Explainability-by-Design' approach for the purposes of the project PLEAD. The taxonomy was built with a view to produce explanations for a wide range of requirements stemming from a variety of regulatory frameworks or policies set at the organizational level either to translate high-level compliance requirements or to meet business needs. The taxonomy comprises nine dimensions. It is used as a stand-alone classifier of explanations conceived as detective controls, in order to aid supportive automated compliance strategies. A machinereadable format of the taxonomy is provided in the form of a light ontology and the benefits of starting the Explainability-by-Design journey with such a taxonomy are demonstrated through a series of examples.

Keywords

Cite

@article{arxiv.2206.04438,
  title  = {A taxonomy of explanations to support Explainability-by-Design},
  author = {Niko Tsakalakis and Sophie Stalla-Bourdillon and Trung Dong Huynh and Luc Moreau},
  journal= {arXiv preprint arXiv:2206.04438},
  year   = {2024}
}
R2 v1 2026-06-24T11:44:54.702Z