Bias Mitigation Techniques

Bias mitigation techniques are systematic approaches used in machine learning and data science to identify, measure, and reduce biases in datasets, algorithms, and models. These techniques aim to ensure fairness, equity, and ethical outcomes by addressing issues like demographic disparities, historical prejudices, or skewed data distributions. They are critical for developing responsible AI systems that avoid discriminatory impacts on protected groups.

Also known as: Fairness Techniques, Bias Reduction Methods, AI Fairness, Algorithmic Fairness, Debiasing

Why learn Bias Mitigation Techniques?

Developers should learn and apply bias mitigation techniques when building machine learning models that affect human decisions, such as in hiring, lending, healthcare, or criminal justice systems, to comply with ethical guidelines and legal regulations like anti-discrimination laws. They are essential for reducing risks of unfair outcomes, improving model trustworthiness, and enhancing social responsibility in AI deployments, particularly in high-stakes applications where biases can lead to significant harm.

Learning Resources

• book: Fairness and Machine Learning
• docs: Google's Responsible AI Practices - Fairness
• course: Coursera: AI Fairness
• docs: IBM AI Fairness 360 Toolkit
• video: YouTube: Introduction to Bias in Machine Learning