AI Bias Reflects Data Choices

AI systems demonstrate significant biases in facial recognition, particularly against individuals who are black or Asian, revealing disparities up to 100 times compared to Caucasian recognition. These biases stem not from the AI itself but from the training data, which is often selected and constructed by humans. The data sets used typically fail to represent the diverse reality, resulting in training inputs overwhelmingly composed of lighter-skinned and male images. This leads to systems that are ineffective for underrepresented groups. Skepticism towards claims of algorithmic bias persists, indicating a lack of understanding of the issue's complexity.