Can AI Ever Overcome Built-In Human Biases?

AI systems today exhibit biases along race, gender, and other factors that reflect societal prejudices and imbalanced training data.
Main causes are lack of diversity in data and teams, and focus on pure accuracy over fairness.
Mitigation tactics like adversarial debiasing, augmented data, and ethics reviews can help reduce bias.
Fundamentally unbiased AI requires rethinking how we build datasets, set objectives, and make ethical design central.
Future challenges include pursuing general AI safely while removing bias, and cross-disciplinary collaboration.
AI …

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