Epistemic welfare and public service media's algorithmic recommender systems: A theoretical framework, operationalization and relevance for governance

Abstract

This contribution introduces the comprehensive framework of epistemic welfare to discuss how public service media (PSM) can engage with algorithmic recommender systems in a manner in keeping with PSM's foundational principles. We contextualize PSM algorithmic recommenders in their tradition of content curation and discuss the challenges PSM face in implementing these systems. We introduce epistemic welfare, a framework based in social epistemology and welfare studies, defined as concerned with creating and maintaining conditions and capabilities for epistemic agency of citizens in the public sphere. We discuss the epistemic standards of reliability, power, fecundity, speed, and efficiency and illustrate the framework's operationalization for the design and implementation of recommenders and its relevance for governance by and of PSM's algorithms. Ensuring that algorithmic recommender systems fit epistemic welfare, we argue, allows PSM to help tackle the epistemic disruptions in the digitalized public sphere.