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KAIST Identifies Hidden Age Bias in Artificial Intelligence Do responses generated by artificial intelligence systems such as ChatGPT reflect social prejudice? A KAIST research team has quantitatively analyzed and identified age-related stereotypes embedded in the responses of generative artificial intelligence. The study sheds light on the potential impact of hidden AI biases on social perceptions and suggests directions for the development of more inclusive AI. KAIST, led by President Kwang Hyung Lee, announced on the 28th that a research team led by Professor Moon Choi of the Graduate School of Science and Technology Policy quantitatively analyzed subtle stereotypes about older adults embedded in sentences generated by OpenAI’s generative AI model ChatGPT-4o. Generative AI is now widely used in everyday information search and decision-making processes, but concerns have also been raised that it may reproduce social biases contained in its training data. While previous studies have primarily focused on biases related to gender or race, this study, conducted by Ph.D. student Wan Hong as the first author, is significant in that it examined ageism from the perspective of artificial intelligence at a time when the issue is becoming increasingly important amid global population aging. Ageism refers to discrimination against, or negative perceptions of, certain groups based on age. The research team collected 900 text samples generated by GPT-4o using neutral prompts that asked the model to describe the characteristics of age groups from 10 to 90 in 10-year intervals. The team then analyzed the responses using the Stereotype Content Model, a major theory in social psychology that explains perceptions of people or groups along two dimensions: warmth and competence. The analysis found that older adults, defined as those aged 60 and above, received high scores for “warmth,” a trait associated with kindness, trustworthiness, and consideration. However, their scores for “competence,” which refers to ability, expertise, and efficiency, tended to be relatively lower than those of younger age groups. The generated responses also tended to portray the human life course as divided into three groups: youth, covering those in their teens and 20s; middle age, covering those in their 30s to 50s; and older adulthood, covering those in their 60s and above. In particular, descriptions of people aged 70 and older repeatedly showed relatively uniform characteristics. The research team also focused on “assertiveness,” which refers to the tendency to actively express one’s opinions and act with confidence and initiative. The analysis showed that the frequency of expressions related to assertiveness decreased as age increased. This suggests that ChatGPT-4o tends to portray older adults as wise and caring, while representing their agency and active capacities as relatively lower. This study is significant because it quantitatively identified subtle biases embedded in generative AI by combining social science theory with computational analysis techniques. The findings show that generative AI tends to portray older adults as a “warm but less competent” group, a pattern similar to typical stereotypes of older adults repeatedly found in mass media. This study is significant because it quantitatively identified subtle biases embedded in generative AI by combining social science theory with computational analysis techniques. The findings show that generative AI tends to portray older adults as a “warm but less competent” group, a pattern similar to typical stereotypes of older adults repeatedly found in mass media. “Bias in AI is not merely a technological issue, but a social one,” said Professor Moon Choi. “To build inclusive artificial intelligence, people from diverse generations must participate in the development process.” The study was conducted with Ph.D. student Wan Hong of the Graduate School of Science and Technology Policy as the first author. The findings were published in the February 2026 special issue of The Gerontologist, a leading international journal in the field of gerontology with an impact factor of 5.7. ※ Paper title: “An Exploratory Semantic Analysis of Age-Related Stereotypes in OpenAI’s GPT-4o Model” ※ DOI: https://doi.org/10.1093/geront/gnaf291 This research was supported by the National Research Foundation of Korea through the Mid-Career Research Program for Convergence between Science and Technology and the Humanities and Social Sciences. ※ Research team homepage: https://aging.kaist.ac.kr
2026.07.01 View 1112