One key approach to striving for neutrality in AI involves diversifying the data sets used for training algorithms. By ensuring a wide representation of perspectives, backgrounds, and scenarios, the AI system can better understand and serve a broader population. However, the presence of inherent biases in historical data and the possibility of overlooking subtle biases means true neutrality remains a challenging goal.
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