Disentangling and Operationalizing AI Fairness at LinkedIn
This addresses the problem of implementing AI fairness in large-scale tech products like LinkedIn, offering a practical approach for industry practitioners, though it is incremental in building on existing fairness concepts.
The paper tackles the challenge of operationalizing AI fairness at LinkedIn's scale by presenting a framework that disentangles equal treatment and equitable product expectations, providing clear guidelines for AI practitioners without imposing trade-offs.
Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are multiple mutually incompatible definitions of fairness but also because determining what is fair depends on the specifics and context of the product where AI is deployed. Moreover, AI practitioners need clarity on what fairness expectations need to be addressed at the AI level. In this paper, we present the evolving AI fairness framework used at LinkedIn to address these three challenges. The framework disentangles AI fairness by separating out equal treatment and equitable product expectations. Rather than imposing a trade-off between these two commonly opposing interpretations of fairness, the framework provides clear guidelines for operationalizing equal AI treatment complemented with a product equity strategy. This paper focuses on the equal AI treatment component of LinkedIn's AI fairness framework, shares the principles that support it, and illustrates their application through a case study. We hope this paper will encourage other big tech companies to join us in sharing their approach to operationalizing AI fairness at scale, so that together we can keep advancing this constantly evolving field.