AbstractThe incorporation of fairness-aware machine learning presents a challenge for creators of personalized systems, such as recommender systems found in e-commerce, social media, and elsewhere. These systems are designed and promulgated as providing services tailored to each individual user’s unique needs. However, fairness may require that other objectives, possibly in conflict with personalization, also be satisfied. The theoretical framework of post-userism, which broadens the focus of design in HCI settings beyond the individual end user, provides an avenue for this integration. However, in adopting this approach, developers will need to offer new, more complex narratives of what personalized systems do and whose needs they serve.
