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In recent years, recommendation and ranking systems have become increasinglypopular on digital platforms. However, previous work has highlighted howpersonalized systems might lead to unintentional harms for users. Practitionersrequire metrics to measure and mitigate these types of harms in productionsystems. To meet this need, many fairness definitions have been introduced andexplored by the RecSys community. Unfortunately, this has led to aproliferation of possible fairness metrics from which practitioners can choose.The increase in volume and complexity of metrics creates a need forpractitioners to deeply understand the nuances of fairness definitions andimplementations. Additionally, practitioners need to understand the ethicalguidelines that accompany these metrics for responsible implementation. Recentwork has shown that there is a proliferation of ethics guidelines and haspointed to the need for more implementation guidance rather than principlesalone. The wide variety of available metrics, coupled with the lack of acceptedstandards or shared knowledge in practice leads to a challenging environmentfor practitioners to navigate. In this position paper, we focus on thiswidening gap between the research community and practitioners concerning theavailability of metrics versus the ability to put them into practice. Weaddress this gap with our current work, which focuses on developing methods tohelp ML practitioners in their decision-making processes when picking fairnessmetrics for recommendation and ranking systems. In our iterative designinterviews, we have already found that practitioners need both practical andreflective guidance when refining fairness constraints. This is especiallysalient given the growing challenge for practitioners to leverage the correctmetrics while balancing complex fairness contexts.

RecSys Fairness Metrics: Many to Use But Which One To Choose?