Schölkopf and AI bias: understanding causation
Understanding causation is vital for addressing AI bias. Bias often arises from confounding variables or spurious correlations in training data, leading models to learn unfair associations. By explicitly modeling causal relationships and understanding how interventions might change outcomes for different groups, we can develop fairer algorithms. The goal is to ensure that models learn the true drivers of outcomes, not societal prejudices.
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