Chapter 9: AI bias, news framing, and mixed-methods approach
Restricted access

Media frame often affects public attitudes toward an emerging scientific field and technology. Bias, which refers to the unfair outcomes based on demographic features, has become the focus of news coverage of artificial intelligence (AI). To understand how AI bias was framed and whether media differed in their framing strategy, this study employs the inductive mixed-method computational approach (ANMTN) proposed by Walter and Ophir (2019) to systematically analyze the news transcripts on AI fairness published by six major television networks in the United States in the past three decades (ABC, CBS, CNN, Fox News, MSNBC, and NBC). The study identifies three dominant frames: benefit and risk, conflict and regulation, and digital journalism. Results show that the conflict and regulation frame becomes the dominant frame in the news coverage after “competing” with the benefit and risk frame in the early stage. Moreover, television networks demonstrate drastically different framing patterns across the three decades. Future directions of media framing research on AI are discussed.

You are not authenticated to view the full text of this chapter or article.

Access options

Get access to the full article by using one of the access options below.

Other access options

Redeem Token

Institutional Login

Log in with Open Athens, Shibboleth, or your institutional credentials

Login via Institutional Access

Personal login

Log in with your Elgar Online account

Login with your Elgar account
Edited by
Handbook