The Bias in Machine Learning

Machine learning models, which are trained on human data, inherently reflect the biases present in that data. Researchers have discovered that these models can develop prejudiced associations mirroring those of humans, such as gender discrimination or racial bias. For example, a model may associate certain professions with specific genders based on historical data, leading to biased interpretations when evaluating candidates for jobs. This issue raises critical concerns about the implications of using AI systems in decision-making processes without addressing these embedded biases.