<abstract xmlns="http://www.w3.org/1999/xhtml">

The now prevalent use of Artificial Intelligence (AI) and specifically machine learning driven models to automate the making of decisions raises novel legal issues. One issue of particular importance arises when the rationale for the automated decision is not readily determinable or traceable by virtue of the complexity of the model used: How can such a decision be legally assessed and substantiated? How can any potential legal liability for a “wrong” decision be properly determined? These questions are being explored by organizations and governments around the world.
A key informant to any analysis in these cases is the extent to which the model in question is “explainable”.
This paper seeks to provide (1) an introductory overview of the technical components of machine learning models in a manner consumable by someone without a computer science or mathematics background, (2) a summary of the Canadian and Vietnamese response to the explainability challenge so far, (3) an analysis of what an ”explanation” is in the scientific and legal domains, and (4) a preliminary legal framework for analyzing the sufficiency of explanation of a particular model and its prediction(s).
</abstract>

The now prevalent use of Artificial Intelligence (AI) and specifically machine learning driven models to automate the making of decisions raises novel legal issues. One issue of particular importance arises when the rationale for the automated decision is not readily determinable or traceable by virtue of the complexity of the model used: How can such a decision be legally assessed and substantiated? How can any potential legal liability for a “wrong” decision be properly determined? These questions are being explored by organizations and governments around the world.
A key informant to any analysis in these cases is the extent to which the model in question is “explainable”.
This paper seeks to provide (1) an introductory overview of the technical components of machine learning models in a manner consumable by someone without a computer science or mathematics background, (2) a summary of the Canadian and Vietnamese response to the explainability challenge so far, (3) an analysis of what an ”explanation” is in the scientific and legal domains, and (4) a preliminary legal framework for analyzing the sufficiency of explanation of a particular model and its prediction(s).

Explainability of Artificial Intelligence Models: Technical Foundations and Legal Principles

Vietnamese Journal of Legal Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The now prevalent use of Artificial Intelligence (AI) and specifically machine learning driven models to automate the making of decisions raises novel legal...

Explainability of Artificial Intelligence Models: Technical Foundations and Legal Principles

Online veröffentlicht: 19. Apr. 2023

Seitenbereich: 1 - 38

DOI: https://doi.org/10.2478/vjls-2022-0006

Schlüsselwörter
artificial intelligence, AI, data protection, automated decision making system, machine learning, Vietnam

© 2022 Jake Van Der Laan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Explainability of Artificial Intelligence Models: Technical Foundations and Legal Principles

Online veröffentlicht: 19. Apr. 2023

Seitenbereich: 1 - 38

DOI: https://doi.org/10.2478/vjls-2022-0006

Schlüsselwörterartificial intelligence, AI, data protection, automated decision making system, machine learning, Vietnam

© 2022 Jake Van Der Laan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
artificial intelligence, AI, data protection, automated decision making system, machine learning, Vietnam