This study examines the political bias of chatbots powered by large language models, namely ChatGPT and Gemini, in the context of the 2024 European Parliament elections. The research focused on the evaluation of political parties represented in the European Parliament across 27 EU Member States by these generative artificial intelligence (AI) systems. The methodology involved daily data collection through standardized prompts on both platforms. The results revealed a stark contrast: while Gemini mostly refused to answer political questions, ChatGPT provided consistent ratings. The analysis showed a significant bias in ChatGPT in favor of left-wing and centrist parties, with the highest ratings for the Greens/European Free Alliance. In contrast, right-wing parties, particularly the Identity and Democracy group, received the lowest ratings. The study identified key factors influencing the ratings, including attitudes toward European integration and perceptions of democratic values. The findings highlight the need for a critical approach to information provided by generative AI systems in a political context and call for more transparency and regulation in this area.

We present a heuristic algorithm, called recombinator-k-means, that can substantially improve the results of k-means optimization. Instead of using simple independent restarts and returning the best result, our scheme performs restarts in batches, using the results of a previous batch as a reservoir of candidates for the new initial starting values (seeds), exploiting the popular k-means++ seeding algorithm to piece them together into new promising initial configurations. Our scheme is general (it only affects the seeding part of the optimization, thus it could be applied even to k-medians or k-medoids, for example), it has no additional costs and it is trivially parallelizable across the restarts of each batch. In some circumstances, it can systematically find better configurations than the best one obtained after 10^4 restarts of a standard scheme. Our implementation is publicly available at https://github.com/carlobaldassi/RecombinatorKMeans.jl.