Continuous Bias Monitoring and Correction

Beyond just improving training data, continuously monitoring for and correcting bias in algorithms is essential. Even with better training data, biases can creep in through model assumptions, unrepresentative features, or skewed testing data. Implementing systems for regular bias audits and employing techniques to correct identified biases ensures that improvements in training data translate into reduced gender bias in practice. This proactive approach is crucial for maintaining fairness in tech solutions over time.