Fairness in Machine Learning: The Principles of Governance

This article from the Vector Institute for Artificial Intelligence explores the critical role of fairness in machine learning governance. As part of a Trustworthy AI series, it aims to help non-technical stakeholders understand and manage ML model risks. The text defines fairness as non-discrimination against protected groups such as race, gender, or age. It highlights ethical, legal, and reputational risks associated with biased algorithms, noting that unfair practices can undermine public trust in AI adoption. The analysis details how bias arises, particularly through historical data reflecting past discriminatory practices like redlining, and through flawed data collection methods. Specific issues such as sample bias, where datasets do not represent the broader population, and measurement bias, resulting from errors in data gathering, are examined. The article emphasizes that even inadvertent bias can lead to significant financial and legal liabilities for organizations. By breaking down these complex technical concepts into plain language, the piece encourages active participation in risk management to ensure automated decisions are made fairly, thereby supporting responsible AI deployment across various industries.