FAccT-Checked: A Narrative Review of Authority Reconfigurations and Retention in AI-Mediated Journalism

TL;DR

This paper critically reviews how AI adoption in journalism shifts editorial authority internally to AI systems and externally to platform providers, impacting fairness, accountability, and transparency.

Contribution

It offers a theoretical framework for understanding authority reconfigurations in AI-mediated journalism and evaluates participatory approaches to authority retention.

Findings

AI-driven authority migration weakens individual agency.

Power shifts to platforms and vendors exacerbate media ecosystem inequalities.

Participatory approaches have both potential and limitations in authority redistribution.

Abstract

Building on recent interpretivist approaches, we conduct a critical narrative review across journalism studies, human-computer interaction, and FAccT scholarship, conceptualizing editorial authority as the conjunction of decision rights, epistemic warrant, and responsibility. We provide a comprehensive theoretical framework for addressing how concerns on fairness, accountability and transparency emerge, interact, and persist within AI mediated journalistic practice. We identify and describe two concurrent authority reconfigurations driven by AI adoption. First, an internal migration of authority, in which editorial judgment is progressively deferred to large language models (LLMs) embedded within newsroom workflows. This migration occurs not through explicit policy decisions, but through interactional, cognitive, and organizational mechanisms that legitimize AI generated outputs while obscuring responsibility and weakening individual and professional agency. Second, we analyze an external migration of authority, whereby decision making power shifts from news organizations toward platforms, vendors, and infrastructural providers that supply AI systems and distribution channels, exacerbating existing power asymmetries within the media ecosystem. Unaddressed, these reconfigurations risk rendering fairness hard to maintain, accountability difficult to assign and transparency performative. We examine participatory approaches to AI design and deployment in journalism as potential mechanisms for retaining or reclaiming editorial authority. We critically assess both their promise and their structural limitations, highlighting how participation can either meaningfully redistribute authority or function as a tokenistic practice that leaves underlying power relations intact.