Missed opportunities in AI regulation: lessons from Canada’s AI and data act

Abstract We interrogate efforts to legislate artificial intelligence (AI) through Canada’s Artificial Intelligence and Data Act (AIDA) and argue it represents a series of missed opportunities that so delayed the Act that it died. We note how much of this bill was explicitly tied to economic development and implicitly tied to a narrow jurisdictional form of shared prosperity. Instead, we contend that the benefits of AI are not shared but disproportionately favour specific groups, in this case, the AI industry. This trend appears typical of many countries’ AI and data regulations, which tend to privilege the few, despite promises to favour the many. We discuss the origins of AIDA, drafted by Canada’s federal Department for Innovation Science and Economic Development (ISED). We then consider four problems: (1) AIDA relied on public trust in a digital and data economy; (2) ISED tried to both regulate and promote AI and data; (3) Public consultation was insufficient for AIDA; and (4) Workers’ rights in Canada and worldwide were excluded in AIDA. Without strong checks and balances built into regulation like AIDA, innovation will fail to deliver on its claims. We recommend the Canadian government and, by extension, other governments invest in an AI act that prioritises: (1) Accountability mechanisms and tools for the public and private sectors; (2) Robust workers’ rights in terms of data handling; and (3) Meaningful public participation in all stages of legislation. These policies are essential to countering wealth concentration in the industry, which would stifle progress and widespread economic growth.

Authors

Institutions

Publication Details

Journal
Data & Policy
Published
2025-01-01
DOI
https://doi.org/10.1017/dap.2025.17
Citations
3
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
7.10

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Missed opportunities in AI regulation: lessons from Canada’s AI and data act

Renée Sieber, Ana Brandusescu
3 citations
Data & Policy
Ethics and Social Impacts of AI
7.10
article

Missed opportunities in AI regulation: lessons from Canada’s AI and data act

Renée Sieber, Ana Brandusescu
article en
3 citations

Abstract

Abstract We interrogate efforts to legislate artificial intelligence (AI) through Canada’s Artificial Intelligence and Data Act (AIDA) and argue it represents a series of missed opportunities that so delayed the Act that it died. We note how much of this bill was explicitly tied to economic development and implicitly tied to a narrow jurisdictional form of shared prosperity. Instead, we contend that the benefits of AI are not shared but disproportionately favour specific groups, in this case, the AI industry. This trend appears typical of many countries’ AI and data regulations, which tend to privilege the few, despite promises to favour the many. We discuss the origins of AIDA, drafted by Canada’s federal Department for Innovation Science and Economic Development (ISED). We then consider four problems: (1) AIDA relied on public trust in a digital and data economy; (2) ISED tried to both regulate and promote AI and data; (3) Public consultation was insufficient for AIDA; and (4) Workers’ rights in Canada and worldwide were excluded in AIDA. Without strong checks and balances built into regulation like AIDA, innovation will fail to deliver on its claims. We recommend the Canadian government and, by extension, other governments invest in an AI act that prioritises: (1) Accountability mechanisms and tools for the public and private sectors; (2) Robust workers’ rights in terms of data handling; and (3) Meaningful public participation in all stages of legislation. These policies are essential to countering wealth concentration in the industry, which would stifle progress and widespread economic growth.

Data & PolicyVol. 7
McGill University (CA)
Social Sciences and Humanities Research Council of Canada
Openalex Percentile: Top 3%
Ethics and Social Impacts of AI
7.10
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.