⚠️ The 'Mirror, Mirror' Bias Trap: A Threat to AI Fairness
Artificial Intelligence (AI) systems learn by reflecting past datasets, which can inadvertently mirror existing biases. This phenomenon is often referred to as the 'Mirror, Mirror' bias trap. When AI models are trained on biased data, they perpetuate these biases, leading to unfair outcomes and perpetuating systemic injustices.
For instance, facial recognition systems have been shown to perform poorly on images of people with darker skin tones, highlighting the risk of racial bias in AI. Similarly, language models have been found to exhibit biases towards certain cultures, ages, and genders.
To break free from the 'Mirror, Mirror' bias trap, AI developers can implement 'Adversarial Training'. This involves intentionally feeding AI systems contrasting and diverse data, designed to challenge existing biases and promote fairness. By doing so, AI models can learn to recognize and adapt to diverse data, leading to more inclusi...
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