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The author argues that AI, particularly from Facebook and Google's algorithms, has worsened US political polarization and expects this trend to continue for the next 15 years, citing several books on AI risks.
A speculative discussion on whether widespread use of LLMs, which are compared to Wikipedia rather than rage-inducing social media algorithms, could reduce societal polarization.
Introduces Cohesion-6K, a manually and ChatGPT-assisted annotated dataset of 6,000 Arabic Facebook posts about the Israeli Occupation of Palestine, spanning conflict to cohesion categories. Analysis shows conflict-oriented posts receive 2-4x more engagement than resolution-oriented ones.
A commentary on how LLMs are polarizing student performance, with some relying on them to skip effort while others excel, leading to both more failures and more top grades.
This paper uses large language models to analyze persuasion dynamics and polarization in Reddit's r/ChangeMyView, finding that empathetic alignment increases belief change while frontal refutation diminishes it.
This paper presents a large-scale audit of recommendation biases in LLM-based content curation across OpenAI, Anthropic, and Google using 540,000 simulated selections from Twitter/X, Bluesky, and Reddit data. The study finds that LLMs systematically amplify polarization, exhibit distinct toxicity handling trade-offs, and show significant political leaning bias favoring left-leaning authors despite right-leaning plurality in datasets.