# Building AI for All: Lessons from GLAAD's New Framework > Source: > Published: 2026-07-11 11:37:45+00:00 # Building AI for All: Lessons from GLAAD's New Framework GLAAD's latest report challenges AI developers to prioritize inclusivity, highlighting how this approach benefits all users. Engaging LGBTQ experiences improves AI's complexity handling. AI systems are everywhere, from the apps on our phones to the algorithms behind the scenes. But who are these systems really serving? GLAAD's latest report, 'Build for Everyone,' takes a hard look at AI's impact on LGBTQ communities, offering a practical roadmap for improvement. ## Why Inclusivity Matters The 'Build for Everyone' report isn’t just another call for inclusivity. It’s a full examination of how AI systems often miss the mark on LGBTQ representation and safety. The press release might boast about AI transformation, but the employee survey likely reveals otherwise. If AI can't accurately handle LGBTQ data, what else is it getting wrong? Leanna Garfield from GLAAD points out how systems trained on biased data aren't just failing LGBTQ folks. They’re failing everyone. When AI can't differentiate between hate speech and reclaimed language, that's an accuracy problem affecting various contexts. It's not just about being fair. it's about making systems smarter and more reliable. ## Real Partnerships, Not Empty Promises GLAAD calls for genuine collaboration between tech companies and civil society. But what does that actually look like? Effective partnerships require more than good intentions. They need early engagement, real access to testing, actionable feedback, and yes, proper compensation. Companies are quick to buy licenses, but nobody tells the team how to involve civil society meaningfully. Simply ticking boxes won’t cut it. Civil society organizations bring expertise and experience that can inform better design and implementation. The gap between the keynote and the cubicle is enormous and bridging it requires real effort. ## Taking the First Step Want to get practical? Start by auditing your [training](/glossary/training) data for LGBTQ representation. It’s not just about quantity, but quality. If your dataset is skewed towards stereotypes, you’re setting up your AI for failure. And if your team lacks the expertise, reach out. There are resources and experts ready to help. GLAAD’s Social Media Safety Program and others provide frameworks that are ready to use. Why should you care? Because AI systems that can navigate complexity serve everyone better. The curb-cut effect isn’t just for infrastructure. it applies to AI too. Inclusivity is a quality standard, not a concession. So what's stopping you from making the first move towards inclusivity today? Get AI news in your inbox Daily digest of what matters in AI.