Algorithmic Bias
Algorithmic bias refers to the systematic and repeatable errors in a computer system that create unfair outcomes, such as discrimination against certain individuals or groups based on their race, gender, age, or other characteristics. These biases can be introduced at various stages of the machine learning pipeline, including data collection, preprocessing, feature selection, model training, and deployment. For example, biased training data can lead to biased models that perpetuate and amplify existing social inequalities. To mitigate algorithmic bias, data scientists and AI practitioners need to adopt a holistic approach that involves careful data curation, diverse representation, fairness metrics, interpretability techniques, and stakeholder engagement. They also need to be aware of the ethical and legal implications of their algorithms and ensure that they align with the values and norms of the society they serve.
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