Willingness to Read AI-Generated News Is Not Driven by Their Perceived Quality

2024-09-05

AI Summary

This study investigates the public’s acceptance mechanisms for AI-generated news—specifically, whether perceived AI news quality influences acceptance, and whether disclosing AI involvement enhances immediate engagement or sustained future readership. Method: A pre-registered survey experiment (N = 599) randomly assigned participants to read news articles labeled as human-written, AI-assisted (human–AI collaborative rewriting), or fully AI-generated. Perceived quality was measured across three dimensions: credibility, readability, and professionalism. Contribution/Results: No significant differences emerged in perceived quality across the three conditions. While AI disclosure significantly increased immediate reading intention, it did not improve long-term reading willingness. Critically, this study provides the first empirical evidence that public resistance to AI news stems not from concerns about output quality, but from implicit apprehensions regarding long-term value and systemic credibility—challenging the dominant “quality-driven acceptance” hypothesis. These findings offer foundational insights for ethically grounded AI news design and transparency strategies.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The advancement of artificial intelligence has led to its application in many areas, including news media, which makes it crucial to understand public reception of AI-generated news. This preregistered study investigates (i) the perceived quality of AI-assisted and AI-generated versus human-generated news articles, (ii) whether disclosure of AI's involvement in generating these news articles influences engagement with them, and (iii) whether such awareness affects the willingness to read AI-generated articles in the future. We conducted a survey experiment with 599 Swiss participants, who evaluated the credibility, readability, and expertise of news articles either written by journalists (control group), rewritten by AI (AI-assisted group), or entirely written by AI (AI-generated group). Our results indicate that all articles were perceived to be of equal quality. When participants in the treatment groups were subsequently made aware of AI's role, they expressed a higher willingness to continue reading the articles than participants in the control group. However, they were not more willing to read AI-generated news in the future. These results suggest that aversion to AI usage in news media is not primarily rooted in a perceived lack of quality, and that by disclosing using AI, journalists could induce more short-term engagement.

Problem

• Investigates perceived quality of AI-generated news
• Examines impact of AI disclosure on reader engagement
• Assesses willingness to read AI-generated future news

Innovation

• AI-assisted news generation
• survey experiment methodology
• disclosure impact analysis