A new study has found that although artificial intelligence (AI) news differs from human news, machine-generated news is not necessarily more biased compared to human news regarding gender and race/ethnicity.

The findings by Seungahn Nah, Dianne Snedaker Chair in Media Trust and research director for the Consortium on Trust in Media and Technology at the University of Florida College of Journalism and Communications (UFCJC), UCLA doctoral student Jun Luo, University of South Florida Assistant Professor Seungbae Kim, UFCJC doctoral students Mo Chen and Renee Mitson, and UCLA Communication Associate Professor Jungseock Joo are featured in “Algorithmic Bias or Algorithmic Reconstruction? A Comparative Analysis Between AI News and Human News” published in the International Journal of Communication, Vol. 18 (2024).

According to the authors, “This study compares human versus GPT-2-generated news in terms of the linguistic features, tone, and bias toward gender and race/ethnicity on two highly controversial issues, namely abortion and immigration, using news transcripts from CNN and Fox News.”

The findings indicate that “(1) human news contains more diversified topics, whereas machine-generate news is more focused on the topic of interest; (2) human and machine news exhibit sharply distinct linguistic word choice; and (3) machine news is relatively more positive and at the same time less biased relative to human news, although there is a disproportionate use of ethnic words related to White.”

They add, “The study provided a foundation for future research to compare machine versus human news concerning automated journalism and news bias. The results and their related implications enable us to pose a fundamental question of whether and how AI-generated news may reflect news bias represented in human news (algorithmic bias) or reconstruct human news in distinct ways (algorithmic reframing or algorithmic reconstruction).”

Posted: February 13, 2024
Category: AI at CJC News, College News, Trust News
Tagged as: Algorithmic Bias, Algorithmic Reconstruction, Consortium on Trust in Media and Technology, Human News, Journal of International Communication, Machine-Generated News, Mo Chen, Renee Mitson, Seungahn Nah