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<p>AI systems aren’t inherently neutral but where do their assumptions about queer people come from in the first place? What happens when queer identities, histories and experiences are missing or misrepresented in the knowledge these systems learn from?</p><p>This presentation dives into the intersection of AI development and Human Resources, presenting key findings from QueerTech’s research initiative. Our data reveals significant gaps, noting that many developers struggle with bias mitigation, citing barriers ranging from institutional indifference to outright prejudice concerning non-binary representation.</p><p>Building inclusive AI isn’t only about what happens when AI is developed or deployed, it also starts with whose knowledge and experiences are represented in the first place. We therefore discuss the urgent need for systemic accountability, from initial design to corporate adoption, and introduce our efforts to guide Canada toward a truly equitable digital future through QueerTech’s Inclusive AI Initiative.<br></p>
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