Uncovering Areas for AI Governance Tools Refinement through Real-World Use Case Analysis from Canada, Chile and Singapore

Kate Kaye
Proceedings of Fourth European Workshop on Algorithmic Fairness, PMLR 294:135-151, 2025.

Abstract

Governments and organizations around the world in most jurisdictions have begun to operationalize principles establishing goals for fair, explainable, robust and trustworthy AI systems through AI governance tools. AI governance tools, socio-technical tools for assessing AI systems and their risks, are used to implement AI governance laws and policies. Understanding the types of measurements and analytical methods embedded within them and evaluating how these tools are implemented in various contexts helps to ensure they effectuate legal and policy goals. The research presented in this paper compares and analyzes the implementation of AI governance tools from Canada, Chile and Singapore. The analysis articulates commonalities among the tools and their implementations and illuminates areas for further analysis and potential refinement in relation to application and interpretation of the metrics and measures used by the tools, implementation of the tools themselves, as well as interests and motivations of tool end users. A key conclusion suggests that although AI governance tools require adequate assessment before they are made available, in some cases, it may be necessary to put some of these tools to use in context in order to articulate otherwise unknown or obscured shortcomings and areas of opportunity for adjustment, refinement, and improvement.

Cite this Paper

BibTeX
@InProceedings{pmlr-v294-kaye25a, title = {Uncovering Areas for AI Governance Tools Refinement through Real-World Use Case Analysis from Canada, Chile and Singapore}, author = {Kaye, Kate}, booktitle = {Proceedings of Fourth European Workshop on Algorithmic Fairness}, pages = {135--151}, year = {2025}, editor = {Weerts, Hilde and Pechenizkiy, Mykola and Allhutter, Doris and Corrêa, Ana Maria and Grote, Thomas and Liem, Cynthia}, volume = {294}, series = {Proceedings of Machine Learning Research}, month = {30 Jun--02 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v294/main/assets/kaye25a/kaye25a.pdf}, url = {https://proceedings.mlr.press/v294/kaye25a.html}, abstract = {Governments and organizations around the world in most jurisdictions have begun to operationalize principles establishing goals for fair, explainable, robust and trustworthy AI systems through AI governance tools. AI governance tools, socio-technical tools for assessing AI systems and their risks, are used to implement AI governance laws and policies. Understanding the types of measurements and analytical methods embedded within them and evaluating how these tools are implemented in various contexts helps to ensure they effectuate legal and policy goals. The research presented in this paper compares and analyzes the implementation of AI governance tools from Canada, Chile and Singapore. The analysis articulates commonalities among the tools and their implementations and illuminates areas for further analysis and potential refinement in relation to application and interpretation of the metrics and measures used by the tools, implementation of the tools themselves, as well as interests and motivations of tool end users. A key conclusion suggests that although AI governance tools require adequate assessment before they are made available, in some cases, it may be necessary to put some of these tools to use in context in order to articulate otherwise unknown or obscured shortcomings and areas of opportunity for adjustment, refinement, and improvement.} }
Endnote
%0 Conference Paper %T Uncovering Areas for AI Governance Tools Refinement through Real-World Use Case Analysis from Canada, Chile and Singapore %A Kate Kaye %B Proceedings of Fourth European Workshop on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2025 %E Hilde Weerts %E Mykola Pechenizkiy %E Doris Allhutter %E Ana Maria Corrêa %E Thomas Grote %E Cynthia Liem %F pmlr-v294-kaye25a %I PMLR %P 135--151 %U https://proceedings.mlr.press/v294/kaye25a.html %V 294 %X Governments and organizations around the world in most jurisdictions have begun to operationalize principles establishing goals for fair, explainable, robust and trustworthy AI systems through AI governance tools. AI governance tools, socio-technical tools for assessing AI systems and their risks, are used to implement AI governance laws and policies. Understanding the types of measurements and analytical methods embedded within them and evaluating how these tools are implemented in various contexts helps to ensure they effectuate legal and policy goals. The research presented in this paper compares and analyzes the implementation of AI governance tools from Canada, Chile and Singapore. The analysis articulates commonalities among the tools and their implementations and illuminates areas for further analysis and potential refinement in relation to application and interpretation of the metrics and measures used by the tools, implementation of the tools themselves, as well as interests and motivations of tool end users. A key conclusion suggests that although AI governance tools require adequate assessment before they are made available, in some cases, it may be necessary to put some of these tools to use in context in order to articulate otherwise unknown or obscured shortcomings and areas of opportunity for adjustment, refinement, and improvement.
APA
Kaye, K.. (2025). Uncovering Areas for AI Governance Tools Refinement through Real-World Use Case Analysis from Canada, Chile and Singapore. Proceedings of Fourth European Workshop on Algorithmic Fairness, in Proceedings of Machine Learning Research 294:135-151 Available from https://proceedings.mlr.press/v294/kaye25a.html.

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