The article discusses the problematic nature of AI systems coded with historical biases, emphasizing the need to decolonize AI. It argues that Western-centric datasets embed cultural assumptions, disadvantaging marginalized communities. This issue is highlighted through examples, like ineffective image recognition for diverse ethnic groups and challenges in natural language processing for non-English languages. The author underscores the importance of interdisciplinary collaboration to address these biases and notes that marginalized voices are often excluded from shaping the technology that affects them, perpetuating cycles of inequality.
AI systems are reflections of the data they are trained on and the values of their creators, often embedding cultural assumptions that disadvantage non-Western populations.
Consider image recognition algorithms that struggle to identify faces from diverse ethnic backgrounds. These failures are not glitches; they are manifestations of a biased system.
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