  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.
