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

2026-04-23

AI Summary

This study examines how artificial intelligence is reshaping editorial authority in journalism through dual trajectories—internal integration into large language models and external delegation to platforms or vendors—thereby threatening fairness, accountability, and substantive transparency. The paper conceptualizes editorial authority as comprising decision-making power, epistemic legitimacy, and responsibility. Employing a critical narrative review methodology, it synthesizes literature from journalism studies, human-computer interaction, and the FAccT (Fairness, Accountability, and Transparency) community to systematically unpack the organizational and cognitive mechanisms through which authority is eroded. The analysis further explores participatory AI design as a potential mechanism for preserving editorial authority, yet cautions that without structural transformation, such approaches risk remaining symbolic, failing to effect meaningful redistribution of power.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• editorial authority
• AI-mediated journalism
• fairness
• accountability
• transparency

Innovation

• editorial authority
• AI-mediated journalism
• authority reconfiguration
• participatory AI
• FAccT