AI Fairness

AI fairness is the principle and practice of ensuring that AI systems do not unfairly advantage or disadvantage particular groups of people. It encompasses the technical methods for measuring fairness, the ethical frameworks for defining it, and the organisational processes for achieving it. Defining fairness is challenging because multiple valid fairness criteria exist, and they often conflict with each other. Demographic parity requires equal outcome rates across groups. Equalised odds requires equal error rates across groups. Individual fairness requires similar individuals to receive similar outcomes. No single definition satisfies all perspectives, and the appropriate choice depends on the context. Fairness assessment involves identifying protected attributes (characteristics like gender, ethnicity, age, disability), measuring how AI outcomes differ across groups defined by these attributes, and determining whether observed disparities are acceptable given the context and applicable regulations.

Fair AI is both an ethical imperative and a business necessity. Regulations increasingly require fairness assessment for AI systems, particularly in high-stakes domains like employment, credit, housing, and insurance. Non-compliance can result in significant penalties and legal action. Beyond compliance, fair AI builds trust with customers, employees, and stakeholders. Organisations known for fair AI practices gain competitive advantage through stronger brand reputation, better talent attraction, and broader market appeal. Implementing fairness requires cross-functional collaboration. Technical teams need to measure and optimise for fairness metrics. Legal teams need to ensure regulatory compliance. Business teams need to define acceptable trade-offs. Ethics boards or committees can provide governance and accountability.