  We introduce a fairness-aware dataset for job recommendation in advertising,
designed to foster research in algorithmic fairness within real-world
scenarios. It was collected and prepared to comply with privacy standards and
business confidentiality. An additional challenge is the lack of access to
protected user attributes such as gender, for which we propose a solution to
obtain a proxy estimate. Despite being anonymized and including a proxy for a
sensitive attribute, our dataset preserves predictive power and maintains a
realistic and challenging benchmark. This dataset addresses a significant gap
in the availability of fairness-focused resources for high-impact domains like
advertising -- the actual impact being having access or not to precious
employment opportunities, where balancing fairness and utility is a common
industrial challenge. We also explore various stages in the advertising process
where unfairness can occur and introduce a method to compute a fair utility
metric for the job recommendations in online systems case from a biased
dataset. Experimental evaluations of bias mitigation techniques on the released
dataset demonstrate potential improvements in fairness and the associated
trade-offs with utility.
