Model Fairness

Model fairness is a concept in machine learning and artificial intelligence that focuses on ensuring algorithms and models do not produce biased or discriminatory outcomes, particularly against protected groups based on attributes like race, gender, or age. It involves techniques and metrics to detect, measure, and mitigate unfairness in predictive models, aiming to promote ethical and equitable AI systems. This field intersects with legal, social, and technical domains to address issues of algorithmic bias and fairness in automated decision-making.

Also known as: Algorithmic Fairness, AI Fairness, Bias Mitigation, Fair ML, Equity in AI

Why learn Model Fairness?

Developers should learn model fairness to build responsible AI systems that comply with regulations like GDPR or anti-discrimination laws, and to avoid harm in high-stakes applications such as hiring, lending, or criminal justice. It is crucial when deploying models that impact people's lives, as unfair models can perpetuate societal biases, lead to legal liabilities, and damage trust in technology. Understanding fairness helps in designing inclusive systems that perform equitably across diverse populations.

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 Algorithmic Fairness