Disentangling and Operationalizing AI Fairness at LinkedIn

Fairness isn't one-size-fits-all: LinkedIn found that different definitions of fairness often contradict each other, and what counts as "fair" depends entirely on the specific product and context where AI is used. This means companies need tailored approaches rather than universal fairness rules.

Separate equal treatment from equity goals: The framework distinguishes between ensuring AI treats people equally (equal treatment) and achieving fair outcomes in products (equity). Rather than forcing a choice between these competing goals, LinkedIn addresses them through different strategies—one focused on the AI system itself and another on broader product design.

AI fairness requires clarity on responsibility: The paper emphasizes that practitioners need clear guidance on which fairness problems should be solved at the AI level versus which ones require product or business strategy decisions. This prevents confusion about who is responsible for addressing different fairness concerns.

Scale demands systematic frameworks: Operating AI fairly at LinkedIn's massive scale requires documented principles and structured approaches, not ad-hoc decisions. The framework provides guidelines that can be consistently applied across many different AI systems and teams.

Industry transparency matters: LinkedIn calls on other major tech companies to publicly share their fairness approaches, arguing that transparency and collective learning will help the field advance and establish better standards for responsible AI deployment.