Artificial intelligence is transforming how we work, engage communities, and deliver services across Canada. Nonprofits, municipalities, healthcare systems, and grassroots organizations are adopting AI to improve efficiency and reach.
But alongside this innovation, we must ask the hard questions. As AI spreads, it can reproduce (or even accelerate) existing racial, environmental, and social inequities.
For volunteer managers, community program leaders, and stewards of public trust, this isn’t theoretical. It is operational and urgent. Canada has the opportunity to lead not only in AI innovation but also in ethical AI governance.
A key principle in responsible AI integration is volunteer-centered service design. Volunteer managers may not design AI tools themselves, but they design volunteer programs and processes, making decisions about how AI is applied in recruitment, scheduling, communications, feedback analysis, and reporting. Keeping volunteers (i.e. the service users) at the centre ensures AI tools enhance engagement, fairness, and inclusion rather than unintentionally marginalize or exclude participants.
Creating Racial and Environmental Disparities
If we fail to act intentionally, AI can deepen inequities, strain marginalized communities, and erode trust. But by centering transparency, accountability, reconciliation, environmental justice, inclusion, and volunteer-centered design, AI can strengthen community work rather than undermine it. Below are four key ways AI is contributing to racial and environmental disparities.
Environmental Racism in the Age of AI
Environmental racism is the targeting of racialized and low-income communities for polluting industries and toxic waste. And, it is not just an American issue. In Canada, Indigenous, Black, and other racialized communities face disproportionate exposure to environmental harm, from boil-water advisories in First Nations communities to industrial facilities near low-income neighbourhoods.
AI infrastructure, like data centres, can contribute to this pattern. They consume massive amounts of electricity and water, and when located in already overburdened communities, they can worsen pollution and public health risks. As provinces compete for tech investment, we must ask:
- Where are data centres being built?
- Who lives nearby?
- Who benefits? And, who bears the cost?
Digital progress still has a physical footprint; and volunteer managers should consider the broader community and environmental impacts when adopting AI tools.
AI Bias in Healthcare and Social Systems
AI is increasingly used in healthcare, social care, and public services. But research shows AI can:
- Underperform for women and racial minorities
- Reflect non-representative (WEIRD) data
- Exclude marginalized populations
- Reinforce systemic bias
In Canada (where for the most part, universal healthcare, diversity and inclusion have often been core commitments) volunteer managers need to ensure AI tools don’t unintentionally harm volunteers or service users, and that equity is actively built into programs and processes.
AI and Volunteer Engagement
AI tools can help volunteer managers, including but not limited to:
- Draft ing surveys and engagement materials
- Translating communications
- Analyzing feedback and participation data
- Improving accessibility through transcription and text-to-speech tools
But there are real risks, inlcuding but not limited to biased outputs, privacy breaches, misinformation, and reduced human connection. Engagement is about relationships, and AI should support, not replace, real human interaction.
Keeping volunteers at the centre means using AI to enhance their experience, not substitute their voices with synthetic feedback.
Why This Matters for Volunteer Managers
Volunteer managers influence how AI can be used in some of these volunteer processes:
- Who is recruited and how
- How applications are screened
- How data is collected and analyzed
- How programs are designed and evaluated
- How digital tools are adopted
If AI is used in any of these processes, you are part of the ethical chain. You have the power to mitigate harm, center volunteer needs, and uphold community trust. with this mind, below are 10 important tips for mitigating some of these risks, and using AI responsibly.
10 Tips for Volunteer Managers Committed to Using AI Responsibly
- Audit Before You Adopt: Ask vendors about training data, bias testing, environmental impact, and data storage location.
- Consider Environmental Impact: Check energy sources powering AI platforms and their alignment with Canada’s climate goals.
- Protect Privacy and Data Sovereignty: Especially for Indigenous or sensitive volunteer and community data.
- Be Transparent: Disclose when AI is used in communications, analysis, or screening.
- Keep Humans in the Loop: Decisions affecting volunteers or communities should always involve human judgment.
- Review for Bias: Check outputs for exclusionary language, cultural insensitivity, or skewed recommendations.
- Do Not Replace Real Participation: Genuine volunteer voices matter; AI-generated simulations are no substitute.
- Build Equity into Procurement: Choose tools and vendors committed to sustainability, fairness, and ethical AI.
- Train Staff and Volunteers: Educate teams on AI’s benefits and risks, including bias, environmental impact, and volunteer-centered design.
- Advocate for Responsible Policy: Support Canadian regulations prioritizing transparency, equity, environmental justice, and meaningful community consultation.
AI is not neutral. It reflects the priorities we build into our programs. By keeping volunteers at the centre of service design, volunteer managers can ensure AI strengthens engagement, equity, and trust; creating progress that truly serves communities, rather than putting them at risk.
The Middle Slice 