AI is when computers or machines are made to do things that normally need human thinking, like understanding language or solving problems. Humans design and program these systems to make them work.

AI has repeatedly shown its capacity to reinforce structural biases, disproportionately affecting Black, Indigenous, disabled, and other marginalized communities. In areas like predictive policing, hiring, and resource allocation, it often deepens existing inequities, making it far from neutral. These risks highlight how AI tends to reflect the oppressive systems from which it emerges. However, groups like Indigenous AI and other justice-oriented movements are exploring ways to develop better, more ethical models. By centering Black, Indigenous, and disability justice leaders, as well as those with lived, intersectional experiences of oppression, AI’s potential could shift toward minimizing harm and addressing inequities. These groups are already working on more culturally informed and just approaches to AI development, but significant challenges remain in ensuring that AI doesn’t perpetuate the very biases it seeks to resolve. The path forward lies in continued efforts to explore and adopt community-centered, justice-driven AI models, though success will depend on continuous critical engagement and harm reduction strategies at every stage. While AI might never be free of bias, these approaches offer possibilities for reducing its harmful impacts.