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ドイツにおけるXとTikTokでシェアされた政治情報の6-7割が極右のものだった
https://mastodon.social/@paul_denton/114047172018809209
MastodonNils Wilcke (@paul_denton@mastodon.social)Révélations de l’ONG Global Witness: en amont des élections allemandes dimanche, 64% du contenu politique recommandé par l'algorithme sur X est lié à l'AFD. Sur TikTok, c'est 78%. C'est monstrueux... https://www.francetvinfo.fr/internet/reseaux-sociaux/allemagne-les-contenus-politiques-de-droite-et-d-extreme-droite-surrepresentes-sur-tiktok-et-x-selon-l-etude-d-une-ong-qui-appelle-l-ue-a-enqueter_7086177.html
#Politique #SocialMedia #AfD #ExtremeDroite #Musk #TikTok #Twitter #Election #Allemagne
SNSは極右の情報が勝つのに、LLMの推論はリベラルが優勢。これって何なんだろう？
ChatGPTの新モデルが少し「右傾化」したとの論文があるが、それでもPolitical compassではかなりリベラル〜左派に位置した結果になっているように見える。
https://www.nature.com/articles/s41599-025-04465-z
Nature“Turning right”? An experimental study on the political value shift in large language models - Humanities and Social Sciences CommunicationsConstructing artificial intelligence that aligns with human values is a crucial challenge, with political values playing a distinctive role among various human value systems. In this study, we adapted the Political Compass Test and combined it with rigorous bootstrapping techniques to create a standardized method for testing political values in AI. This approach was applied to multiple versions of ChatGPT, utilizing a dataset of over 3000 tests to ensure robustness. Our findings reveal that while newer versions of ChatGPT consistently maintain values within the libertarian-left quadrant, there is a statistically significant rightward shift in political values over time, a phenomenon we term a ‘value shift’ in large language models. This shift is particularly noteworthy given the widespread use of LLMs and their potential influence on societal values. Importantly, our study controlled for factors such as user interaction and language, and the observed shifts were not directly linked to changes in training datasets. While this research provides valuable insights into the dynamic nature of value alignment in AI, it also underscores limitations, including the challenge of isolating all external variables that may contribute to these shifts. These findings suggest a need for continuous monitoring of AI systems to ensure ethical value alignment, particularly as they increasingly integrate into human decision-making and knowledge systems.