29.05.2026

Diversity in AI Research

How do we ensure that AI and autonomous systems are safe for everyone, not just a select few?

A new feature from the Ibrahim Habli, explores why diversity is just as critical to AI safety as technical code. Because AI learns from historical data, it can easily absorb and amplify human biases. If design teams or data sets lack diverse perspectives, the technology risks failing when deployed in the real world.

To solve this, researchers are using human-centric frameworks like PRAISE and SACE to ensure AI aligns with varied cultural, social, and ethical norms. True safety isn’t just about avoiding technical glitches—it’s about ensuring technology functions equitably for all.