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Adding algorithms in the newsroom can help build trust

June 9, 2020 | By Rande Price, Research VP – DCN

Building trust in the news media is a careful balance in accuracy, bias, and transparency. Unfortunately, news brands today don’t live in isolation. Adding social media to the mix alters this balance with its potential amplification of inaccurate, out-right fake, and politically manipulated news stories. With a surge in digital news consumption, it is important to examine new strategies to renew consumer trust.

Newly honored research from the University of Florida  Consortium on Trust in Media and Technology offers insight into the value of adding algorithmic reporting to human bylines to increase positive perception of news coverage. T. Franklin Waddell research, Can an Algorithm Reduce the Perceived Bias of News? Testing the Effect of Machine Attribution on News Readers’ Evaluations of Bias, Anthropomorphism, and Credibility, examines the premise that machine-based news stories are objective and “free from bias.”

Adding machines to the process

Waddell conducted an online experiment to test this premise. He tested author attribution: journalist vs. algorithm vs. combined authorship from two news outlets (MSNBC vs. Fox News) on two story topics (Khan Conflict vs. Paris Accord). In all, there were 612 participants who were asked to read a news article that was written by either a journalist, an algorithm, or by a journalist and algorithm together.

After reading the article, the respondents were asked questions on article credibility, perceived bias, and the personification of human characteristics. Waddell’s research also explored whether news attributed to an automated author is perceived as less biased and more credible than news attributed to a human author.

Credibility results

The results of the study showed that algorithms were perceived as less biased than human authors. Machine attribution decreased perceptions of bias, which then related to message credibility. In other words, the research found that news automation aids credibility and helps to reduce the perception of bias.

More importantly, machine and human sources combined offered a more favorable credibility outcome and the perceived bias was less than a human or machine source alone.


The research also discusses the importance of messaging this combined journalistic approach to news consumers.  Waddell found a positive impact on message credibility when using a combined byline of journalist and machine. Additional research needs to be conducted on specific labeling and consistency to accurately describe the degree of automation used as well as to heighten the credibility of the news products.

Waddell’s findings that bias perceptions lessen when news is attributed to a combined partnership of human journalist and machine automation is significant. While there are concerns about biases built in to AI itself, these research insights offer a promising step that could have a positive impact on consumer trust in media news. Automation alone does not appear to be the answer. But, along with historical context, background information and fact checking, it offers a starting point. And it is critical for the news media to explore ways in which it can reinforce the credibility of quality journalism.

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