How can we address bias in machine learning models?
An example of an AI chatbot conversation about addressing bias in machine learning.
How can we address bias in machine learning models?
Addressing bias in ML involves techniques like data preprocessing, algorithm adjustments, and regular audits.
What are some common sources of bias in training data?
Common sources include historical biases, unrepresentative samples, and labeling errors.
Can you give examples of algorithms that help reduce bias?
Yes, algorithms like re-weighting, adversarial debiasing, and fairness constraints can help reduce bias.
How do we measure bias in machine learning models?
Bias can be measured using metrics like disparate impact, equal opportunity, and predictive parity.
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