Khosrowi, Donal and Beck, Lukas (2026) Like It or Not — Recommender Systems Lack a Coherent Normative Foundation. The 2026 ACM Conference on Fairness, Accountability, and Transparency (FAccT '26).
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Abstract
Recommender Systems (RS) are among the most widely-deployed types of algorithmic systems, shaping the contents and items that billions of users see, engage with, and purchase on a daily basis. A dominant narrative in the literature characterizes RS as estimating user’s preferences and using this information to recommend good items for users. What is striking about this narrative is that it offers a welfare consequentialist justification for RS, in which preference satisfaction is the core welfare criterion that tells us how to rank outcomes in terms of agents' welfare. We argue that this justification is severely undertheorized and defective. More specifically, RS research currently lacks a coherent conceptualization of the relationship between preference, choice, and welfare. As a consequence, most RS research lacks an explicit and coherent theory of welfare-relevant preferences. Instead, it gestures towards outdated economic theories that have since been contravened by decades of research in the behavioral sciences and philosophy. To make our case, we draw out several interconnected challenges that call into question whether RS deals in genuine information about welfare-relevant preferences. To address these challenges, we sketch the contours of an interdisciplinary research program to put RS on coherent welfare-theoretical foundations.
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