Age and gender distortion in online media and large language models
Descripción general
Resumen del artículo
This study reveals widespread age- and gender-related distortion across online media (images, videos, text) and large language models (LLMs). It found that women are consistently depicted as younger than men, especially in higher-status roles, and demonstrated how mainstream algorithms amplify these biases, influencing people's beliefs and even AI-driven hiring recommendations. The research combined large-scale data analysis with human experiments and LLM audits to show these biases are pervasive and disconnect from real-world demographics.
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The internet and smart computer programs like ChatGPT often show women as younger and men as older than they really are, especially for important jobs. This makes people think women are less experienced and affects who gets hired, even in AI.
Posibles conflictos de intereses
None identified. Authors are affiliated with academic institutions (Stanford University, University of California Berkeley, University of Oxford, Autonomy Institute) and disclose academic funding sources. The study audits products from Google and OpenAI, but authors do not appear to be employees of these companies.
Limitaciones identificadas
Explicación de la calificación
This paper presents strong, multi-modal evidence of age and gender bias across a vast array of online data and algorithms, including LLMs and search engines. The methodology is robust, combining large-scale observational data, a human participant experiment, and an AI audit. The findings are highly significant for understanding and addressing algorithmic bias and societal inequality, despite some limitations inherent in crowdsourced data and synthetic AI experiments.
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