Descripció del projecte
Artificial Intelligence models, such as Recommender Systems (RS) and Large Language Models (LLM), increasingly mediate access to political information, raising concerns about their impact on democracy, particularly during elections. However, despite the growing body of research on AI systems, a cross-platform and cross-country comparison of the impact of different AI systems within the same election still needs to be included. This project seeks to investigate the effects of RS and LLM on the news landscape using the 2024 EU elections as a case study.
The research will examine how prominent politicians are presented in different nations and platforms by focusing on YouTube, TikTok’s RS, and BingChat, a new LLM-powered search engine that integrates BingSearch with ChatGPT. Additionally, the project will assess potential biases, risks, and legal implications for European Regulations.
By employing a combination of algorithmic auditing techniques and digital methods to collect the data and a combination of computational methods to analyze them, the research aims to provide insights into how AI systems shape the diffusion of political candidates’ information and offer recommendations for assessing compliance with mandatory Risk Assessment and independent audits outlined in the Digital Services Act. The findings from this study will be presented through a series of scholarly papers and a final thesis to foster discussion and collaboration across academia, industry, and regulatory bodies.
In particular, the project aims to answer the following research questions:
RQ1: Explore how AI systems shape political candidates’ information diffusion during elections.
RQ2: Realize independent algorithmic audits with new cross-platform methodologies for comparing RSs and LLM-powered search engines.
RQ3: Provide recommendations to independently assess how online platforms comply with the mandatory Risk Assessment for foreseeable adverse effects on electoral processes (DSA, art. 34) and the best techniques to perform the mandatory independent audits (DSA, art. 37).
To that purpose, the research will employ a combination of algorithmic auditing techniques (Bandy, 2021) and digital methods to conduct a cross-platform examination (Rogers, 2017. Venturini, 2018), replicating the same approach across the two most prevalent video-sharing platforms, Youtube and TikTok, and the ChatGPT-powered BingChat.
The data collection will consider at least two countries during the lead-up (three months) to and aftermath (two weeks) of their respective European Elections. This will provide a comprehensive view of how AI systems re-mediate the election candidates’ information. For the part related to recommender systems, a list of politically categorized news actors will be used to recreate the users’ browsing activity of automated accounts or “suck puppets” (Sandvig, 2014) and simulate their echo chambers to register their filter bubbles, consequently (Zimmer, 2019). The recommendations will be collected with passive scraping tools (Tracking Exposed and DMI). This technology is also adaptable to crowdsourced data donations for a parallel collection involving actual voters. Alternatively, a Data Subject Access Request (GDPR, art. 15) will be used for the same scope.
Furthermore, the research will utilize a mix of topic modeling, network analysis, hypothesis testing, and other methodologies, such as sentiment analysis, clustering, and deep learning, to compare the data collected from different recommender systems and large language models (Romano, 2022). By incorporating these diverse analytical techniques, the research will provide a comprehensive and holistic understanding of the impact of AI systems on political discourse during European Elections and identify potential biases and discrepancies in the recommendations and outputs generated by these systems. This in-depth analysis will ultimately contribute to the development of more transparent and accountable AI technologies in the realm of politics and elections.
References
1)Rogers, R. (2017). Digital methods for cross-platform analysis. The SAGE handbook of social media, 91-110.
2)Romano, S., Faddoul, M., Rama, I., Giorgi, G., Kerby, N,. (2022, July). French Elections 2022: The visibility of French candidates on TikTok and YouTube Search Engines. Tracking Exposed, https://tracking.exposed/pdf/french-elections-2022.pdf
3)Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014). Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and discrimination: Converting critical concerns into productive inquiry, 22(2014), 4349-4357.
4)Venturini, T., Bounegru, L., Gray, J., & Rogers, R. (2018). A reality check (list) for digital methods. New media & society, 20(11), 4195-4217.
5)Zimmer, F., Scheibe, K., Stock, M., & Stock, W. G. (2019). Fake news in social media: Bad algorithms or biased users?. Journal of Information Science Theory and Practice, 7(2), 40-53.
