Artificial intelligence is increasingly promoted as a solution for social problems, but its effectiveness is questioned due to inherent structural inequities. Authors like Sayash Kapoor and Arvind Narayanan compare the hype to "snake oil," while Kate Crawford highlights AI's roots in extractive industries that amplify existing inequalities. Case studies reveal that AI's success in addressing social issues, particularly in the developing world, depends less on the technology itself and more on the surrounding human infrastructure and institutional support. AI systems often struggle with local contexts, biases, and non-Western languages, underscoring the need for careful design and implementation. AI
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RANK_REASON The article discusses the limitations and potential pitfalls of using AI for social good, drawing on expert opinions and case studies, which falls under commentary.
