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  • Mitigating Dialect Bias

Mitigating Dialect Bias

Can Large Language Models be developed and deployed in a socially responsible way that prevents harm and ensures digital equity for diverse linguistic communities?

The global proliferation of Large Language Models (LLMs) is a top priority for technology leaders and governments. However, their widespread integration into society presents a critical safety risk for millions. For the over 140 million speakers of Nigerian Pidgin English, these advanced AI systems can lead to censorship, discrimination, and digital exclusion.

Because of systems-level barriers, such as the vast underrepresentation of marginalized dialects in training data, the lack of culturally-aware evaluation tools, and undeveloped regulatory frameworks, it has been difficult to scale safe and inclusive AI systems from a single language to entire global populations. Communities whose dialects are not prioritized in AI development are less likely to benefit from technological advances and are more likely to be harmed by biased algorithms, leading to what can be termed a “cycle of digital exclusion.”

Dismantling barriers to safe and equitable AI requires concerted efforts by developers, researchers and policymakers. However, these efforts are hampered by the lack of analytic tools that can accurately identify and mitigate the population-wide risks of dialect bias, where a user's language variety is systematically misinterpreted as toxic, inappropriate or harmful.

This Solution Network will focus on the socially responsible, citizen-centered co-creation of dialect bias benchmarks, mitigation tools, and policy solutions, working with a diverse Canadian-African team and directly with Nigerian Pidgin-speaking communities.

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Founded

2025

Supporters

International Development Research Centre, CIFAR

CIFAR Contact

Gagan Gill
Associate Director, AI Safety

List of Members:

Deborah Damilola Adeyemo

Deborah Damilola Adeyemo

Solution Network Member

Canadian AI Safety Institute Research Program
Mitigating Dialect Bias
University of Ibadan
Nigeria
Wenhu Chen

Wenhu Chen

Canada CIFAR AI Chair
Solution Network Member

Canadian AI Safety Institute Research Program
Mitigating Dialect Bias
Pan-Canadian AI Strategy
University of Waterloo
Canada
Thomas-Michael Emeka Chukwumezie

Thomas-Michael Emeka Chukwumezie

Solution Network Member

Canadian AI Safety Institute Research Program
Mitigating Dialect Bias
University of Nigeria
Nigeria
Blessing Ogbuokiri

Blessing Ogbuokiri

Solution Network Co-Director

Canadian AI Safety Institute Research Program
Mitigating Dialect Bias
Brock University
Canada
Laleh Seyyed-Kalantari

Laleh Seyyed-Kalantari

Solution Network Co-Director

Canadian AI Safety Institute Research Program
Mitigating Dialect Bias
Vector Institute
York University
Canada
Collins Nnalue Udanor

Collins Nnalue Udanor

Solution Network Member

Canadian AI Safety Institute Research Program
Mitigating Dialect Bias
University of Nigeria
Nigeria
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