Since the federal government released the "AI for All" strategy on June 4, I have been tirelessly drafting an open letter from credentialed scholars, organizations, and practitioners within the humanities and social sciences. For the past week, I have dedicated myself to this effort, aiming to ensure our voices are heard and valued as well as to extend the invitation from the general public to participate in this petition. 

The "AI for All" strategy, while well-intentioned (which I have acknowledged elsewhere:  Canada just published its big AI plan and The questions Canada's new national AI strategy raises, and then sidesteps), has consistently centred the commercial and consulting sectors. It has marginalized the very disciplines best positioned to address the critical societal questions that artificial intelligence brings to life: philosophy, history, archival studies, literature, Indigenous studies, sociology, and beyond, alongside the general public whose lives these systems are already reshaping.

The letter makes 6 demands of the federal government:

1. Guaranteed representation of humanities and social science scholars on every federal AI advisory body, with membership and minutes made public.
2. Funded, independent impact studies of how AI is reshaping the lives of the general public: workers, families, communities, and students whose fields of study have been disrupted mid-degree, reported to Parliament annually.
3. Mandatory social, cultural, and ethical impact assessment before public funds procure or subsidize AI systems.
4. Research funding parity for critical AI scholarship in the humanities and social sciences.
5. AI literacy programs that teach citizens to question these systems, not merely operate them.
6. Consultation processes in which affected communities can alter outcomes, not merely be cited as heard.

The social sciences and humanities offer essential insights into the ethical, historical, and social dimensions of AI; insights that are crucial for crafting balanced and inclusive policies. By engaging experts from these fields, alongside the citizens who live with these systems daily, we can pave the way for a future where AI not only advances technologically but also aligns with human values and societal well-being.

Join us in urging the government to recognize and integrate the crucial contributions of the humanities, the social sciences, and the public itself in AI policymaking. Your signature will lend weight to our collective call for a more balanced approach. Sign the petition and stand with us in advocating for an AI future that is truly for "ALL".

MORE DETAILS 

What our disciplines bring to the table
This is not a plea for inclusion as courtesy. It is a statement of competence. The hardest questions in AI governance are not engineering questions, and the people best situated to answer them have spent careers doing exactly that:

• Philosophy: Ethics, epistemology, and philosophy of mind are the core of every debate about machine “intelligence,” moral responsibility, consent, and value alignment. Philosophers built the conceptual tools this field borrows daily, usually without citation and often without rigour.
• History: Historians of the industrial revolution have already documented what happens when a society lets capital dictate the terms of technological transition: who absorbed the costs, how long “temporary” displacement lasted, and which protections were only won after avoidable suffering. We are watching the same patterns repeat in real time, and the strategy shows no sign of having read that record.
• Archival studies and library and information science: Archivists and information scientists understand provenance, classification, and the politics of the record. They can explain precisely how bias enters training data, whose voices are systematically absent from the corpora these models learn from, and what is lost when synthetic text floods the documentary record of a nation.
• Literature, writing, and the arts: Writers and literary scholars understand language as a human practice: authorship, voice, originality, and the cultural labour now being extracted without consent or compensation to train commercial systems. They can also tell us what a society loses when expression is automated.
• Linguistics: Linguists can say what these models actually do with language, where fluency diverges from understanding, and what mass deployment means for endangered languages, including the Indigenous languages this strategy claims to protect.
• Sociology and anthropology: These fields hold the methods, ethnography, longitudinal study, and labour research, needed to produce the impact evidence the strategy lacks: who is displaced, who is surveilled, who benefits, and how communities actually live with these systems.
• Indigenous studies: Scholars of Indigenous data sovereignty have developed frameworks, including OCAP principles, that answer the strategy's own sovereignty rhetoric with substance: data governance grounded in consent and community ownership rather than national branding.
• Black studies, feminist studies, and disability studies: Researchers in these fields documented algorithmic discrimination years before industry acknowledged it: facial recognition that fails darker skin, hiring tools that penalize women, systems that encode ableist defaults. The communities most harmed by AI are the ones least represented in its governance, and these scholars are their evidentiary record.
• Education: Education researchers can assess what AI is doing inside classrooms right now: to assessment, to academic integrity, to the development of the very critical thinking a democratic society requires.
• Law, political science, and political economy: These disciplines understand regulatory capture, the limits of voluntary compliance, and why a strategy whose protections are promissory while its subsidies are immediate will deliver the subsidies and defer the protections.