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Вперед к истокам: обзор подходов к изучению политических предубеждений больших языковых моделей
Political bias of Large Language Models has frequently become a topic for scientific investigation. Most of the researchers tend to compete in inventing more original ways of identifying bias rather than posing new research questions related to it besides “Is this model politically biased?” and “What is the character of its bias?”. To properly evaluate possible influence of the models on the political reality and finding answers to some questions regarding regulation of Artificial Intelligence it is essential to be able to study the linkage between the bias and its cause. With regard to how the question of dependence between the identified bias and its possible source is ad- dressed I have grouped the approaches to studying political bias of LLMs into three clusters: approaches that use political orientation surveys and questionnaires, studies devoted to investigating different ways of creating prompts and models’ responses and their interdependence, and interdisciplinary research in which manipulations with possible sources of LLMs’ political bias is conducted. The latter research trajectory seems to be the most promising one, despite its current unpopularity. Yet, it is impossible to advance in this trajectory without it being complemented by further developments in the approaches in the first two clusters. In studying the issue of LLM bias, not only computer science specialists but also philosophers and political scientists and other experts in the social sciences should be involved. Political biases at the intersection of LLM and other generative AI technologies – in particular, technologies for generating images based on prompts composed in natural language, recommendation algorithms, etc. – also require separate research. From a regulatory standpoint, further progress in mitigating, eradicating, and controlling political biases in algorithmic tools will require providing researchers with greater access to existing and actively used technologies. Furthermore, it appears necessary to establish specialized institutions dedicated to research on AI at the intersection of computer science, ethics, the philosophy of mind, neurocognitive and social sciences.