Building AI Systems That Work For Everyone

2026-07-01 · Source: Partnership on AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI Ethics & Responsible AI · Depth: Intermediate, short

Summary

GLAAD's "Build for Everyone: A Framework for LGBTQ Representation and Safety in AI" report, released July 1, 2026, offers a comprehensive examination of AI systems' impact on LGBTQ individuals and provides a practical roadmap for the tech industry. The report details how current AI systems fall short on LGBTQ safety, privacy, and inclusion across the entire AI lifecycle, from foundation model training to product deployment and content moderation. It presents specific recommendations for developers, deployers, and policymakers, arguing that these issues stem from design choices that can be improved. A core principle highlighted is that designing for LGBTQ communities, which often involves navigating complex language, identity, and context, ultimately leads to more accurate and trustworthy AI systems for all users, akin to the "curb-cut effect." The report also advocates for genuine, compensated collaboration between AI companies and civil society, stressing early engagement, data access for third-party auditing, and integration of feedback into product roadmaps.

Key takeaway

For AI engineers and product managers building new systems, prioritize inclusive design from the outset. Your training data directly impacts system accuracy and fairness for all users, not just marginalized groups. Audit your datasets for diverse LGBTQ representation and engage civil society experts early to integrate their feedback. This proactive approach will yield more robust, trustworthy AI products and prevent costly retrofits later.

Key insights

Inclusive AI design for LGBTQ communities enhances system quality and safety for all users.

Principles

• Inclusive design improves AI accuracy for everyone.
• Biased training data creates systemic accuracy problems.
• Effective civil society collaboration requires timing, access, integration, compensation.

Method

Engage civil society early, provide data access for auditing, integrate feedback into product roadmaps, and compensate experts fairly. Audit training data for diverse LGBTQ representation.

In practice

• Audit training data for diverse LGBTQ representation.
• Engage subject-matter experts for data evaluation.
• Consult GLAAD's Social Media Safety Program.

Topics

• AI Ethics
• Inclusive Design
• LGBTQ Representation
• Data Bias
• Civil Society Collaboration
• Content Moderation

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, AI Ethicist, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by Partnership on AI.