Will we fix AI bias against LGBTQ+ users?
I'm Alice Hunsberger. Trust & Safety Insider is my weekly rundown on the topics, industry trends and workplace strategies that trust and safety professionals need to know about to do their job.
I shared a post this week shouting out some amazing women making things happen in T&S, online governance and AI. And I've been thinking since about the many individual contributions that practitioners of all kinds — including many EiM readers — make to help the field move forwards.
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GLAAD's new report builds on that idea that it's people like you and me who will decide whether AI repeats old moderation dynamics or fixes them. That's heartening, at least to me. So, without further ado, here we go! — Alice
Fixed classifiers meet steerability
If we’ve known implicitly for a while that AI systems are at risk of perpetuating anti-LGBTQ+ bias in online moderation, a new report from GLAAD makes it explicit.
Build for Everyone, which was published last week, is a careful account of how AI systems are failing LGBTQ+ people. (Disclaimer: I gave feedback on it and I'm in the acknowledgements, so I'm not a neutral party here).
The report itself and the early reporting on it have, understandably, led with the harm that can be caused by AI systems that aren’t built with marginalised communities in mind. That’s incredibly important, and the recommendations in the report are really good. You should go read the whole thing.
However, there's a second part to this story that is getting less attention: AI is the best chance the LGBTQ+ community has had in years to fix moderation problems that traditional moderation systems could never solve.
Bias isn't new, and that's the point
Part of what makes Build for Everyone a good read is the way it chronicles LGBTQ+ moderation failures over the years: Meta's Llama 4 recommending conversion "therapy" in response to user queries, Grok generating non-consensual intimate imagery, and the long-running problem of automated systems suppressing legitimate LGBTQ+ content through shadowbanning, demonetization, and wrongful removals.
It's easy to read all of this as a story about AI introducing bias into moderation, but I’d argue that isn't quite right. The bias that GLAAD documents isn’t new; it’s part and parcel of human review and it also lives in the fixed machine learning classifiers that replaced some of that human work.
I wrote about this back in 2024, in a Pride-season guide to protecting LGBTQ+ users. A lot of what I flagged then was about human moderators: the need for anti-bias training, the importance of internal documentation of reclaimed language and the pattern where trans users get flagged as "fake" by bigots and disproportionately banned. None of that was an AI problem. It was a people problem that automation then inherited, and I've written separately about how these systems learn the wrong shortcuts and perpetuate familiar patterns.
So it’s not that AI made moderation biased against LGBTQ+ people. Bias was already there, and AI made it both more visible and, for the first time, more directly addressable.