Algorithmic Journalism and Ideological Polarization: An Experimental Work Around ChatGPT and the Production of Politically Oriented Information

Abstract

Artificial intelligence (AI) is one of the emerging technologies that is developing with ever greater intensity and in an ever-increasing number of domains, often overturning the features of these domains. In the domain of journalism, generative AI has become a tool used to write texts and articles with potential implications on ethics and on the issue of transparency (Diakopoulos, Koliska, 2017) together with a possible reconfiguration of the perimeter and of the foundations of making information, with the oscillation between different options and positions (Schapals, Porlezza, 2020). The scientific literature on the subject is expanding and the discussion among decision makers is becoming more intense (most recently with the AI Act of the European Parliament). Actually, journalism has represented one of the professions most characterized by the relationship with technology, and most significantly modified by it in its production processes and business models (Pavlik, 2000). Natural Language Generation (NLG) software based on AI algorithms has contributed particularly significantly to spreading the perception of a “paradigm shift” among insiders and information operators. In fact, various theses and arguments have been developed around such software, which can include political-ideological and ethical-philosophical evaluations. Therefore, in this work we propose an experimental work, based on a mixed-method methodology, which starts from the following research question: is there a prevalent political orientation of AI-based generative software? Or, better yet, can we arrive, on certain topics, to verify a propensity of the machine to generate “polarized” articles classifiable along the right-left axis in relation to the subject of the discussion? And, therefore, can “automated” journalism also lead to the necessary production of articles with a predefined orientation and thesis? To verify this research hypothesis, we intend to have an AI-based NLG platform (e.g., ChatGPT) generate some articles on three selected topics with reference to the most recent Italian and international political debate, also investigating the effect of the cheat sheet indications on the polarization of the articles: a. immigration management policies; b. minimum wage; c. adoption of children by homoparental couples. That is, topics usually treated in a highly polarized way in the contemporary transitional post-public sphere (Schlesinger, 2020), so as to empirically test whether the automation in the production of articles is free from political evaluations or whether it turns out to be influenced by a dominant (or mostly distributed) political orientation/thought within the vast dataset of document sources that form the basis of the AI system's training.BibliographyDiakopoulos, Nicholas, and Michael Koliska. 2017. Algorithmic transparency in the news media. Digital Journalism, 5: 809–28. Pavlik, John. 2000. The impact of technology on journalism. Journalism Studies 1: 229–37. Schapals, Aljosha Karim, and Colin Porlezza. 2020. Assistance or resistance? Evaluating the intersection of automated journalism and journalistic role conceptions. Media and Communication 8: 16–26. Schlesinger, P. (2020). After the post-public sphere?. Media Culture and Society, 42(7-8), pp. 1545-1563.

Keywords: Generative AI, journalism, ChatGPT

DOI: 10.54941/ahfe1005943