Measuring Algorithmic Partisanship via Zero-Shot Classification and Its Implications on Political Discourse
It addresses the problem of political bias in AI systems affecting public discourse, with incremental improvements in measurement methodology.
The study measured algorithmic political partisanship in six large language models using zero-shot classification across ideological alignment, topicality, sentiment, and objectivity, finding an amplified liberal-authoritarian alignment with 1800 model responses analyzed.
Amidst the rapid normalization of generative artificial intelligence (GAI), intelligent systems have come to dominate political discourse across information media. However, internalized political biases stemming from training data skews, human prejudice, and algorithmic flaws continue to plague this novel technology. This study employs a zero-shot classification approach to evaluate algorithmic political partisanship through a methodical combination of ideological alignment, topicality, response sentiment, and objectivity. A total of 1800 model responses across six mainstream large language models (LLMs) were individually input into four distinct fine-tuned classification algorithms, each responsible for computing one of the aforementioned metrics. The results show an amplified liberal-authoritarian alignment across the six LLMs evaluated, with notable instances of reasoning supersessions and canned refusals. The study subsequently highlights the psychological influences underpinning human-computer interactions and how intrinsic biases can permeate public discourse. The resulting distortion of the political landscape can ultimately manifest as conformity or polarization, depending on the region's pre-existing socio-political structures.