AI's Economic Blind Spot plus AI Expansion
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Recent research highlights a critical "economic blind spot" in AI, particularly computer vision. While AI is often touted as objective, it struggles with images from lower socioeconomic backgrounds. Accuracy decreases, and negative labels increase. This isn't theoretical; AI's use in home valuation and urban planning risks amplifying societal inequalities. Even advanced AI models show this bias, associating wealth with positive concepts and poverty with negative ones.
The root cause lies in skewed training datasets, overrepresenting affluent areas. Addressing this requires rethinking AI development, prioritizing diverse data, and acknowledging human biases. Failing to do so could lead to AI reinforcing existing inequalities.