Popular and news media often portray teenagers with sensationalism, as both a risk to society and at risk from society. As AI begins to absorb some of the epistemic functions of traditional media, we study how teenagers in two countries speaking two languages: 1) are depicted by AI, and 2) how they would prefer to be depicted. Specifically, we study the biases about teenagers learned by static word embeddings (SWEs) and generative language models (GLMs), comparing these with the perspectives of adolescents living in the U.S. and Nepal. We find English-language SWEs associate teenagers with societal problems, and more than 50% of the 1,000 words most associated with teenagers in the pretrained GloVe SWE reflect such problems. Given prompts about teenagers, 30% of outputs from GPT2-XL and 29% from LLaMA-2-7B GLMs discuss societal problems, most commonly violence, but also drug use, mental illness, and sexual taboo. Nepali models, while not free of such associations, are less dominated by social problems. Data from workshops with N=13 U.S. adolescents and N=18 Nepalese adolescents show that AI presentations are disconnected from teenage life, which revolves around activities like school and friendship. Participant ratings of how well 20 trait words describe teens are decorrelated from SWE associations, with Pearson's r=.02, n.s. in English FastText and r=.06, n.s. in GloVe; and r=.06, n.s. in Nepali FastText and r=-.23, n.s. in GloVe. U.S. participants suggested AI could fairly present teens by highlighting diversity, while Nepalese participants centered positivity. Participants were optimistic that, if it learned from adolescents, rather than media sources, AI could help mitigate stereotypes. Our work offers an understanding of the ways SWEs and GLMs misrepresent a developmentally vulnerable group and provides a template for less sensationalized characterization.

翻译：大众媒体和新闻媒体常以耸人听闻的方式描绘青少年，既将其视为社会威胁，又视其为社会风险的承受者。随着人工智能开始承担传统媒体的部分认知功能，我们研究了来自两个国家、使用两种语言的青少年：1）如何被人工智能描绘；2）他们更希望如何被描绘。具体而言，我们研究了静态词嵌入模型和生成式语言模型所习得的关于青少年的偏见，并将其与美国和尼泊尔青少年的观点进行比较。研究发现，英语静态词嵌入模型将青少年与社会问题相关联，在预训练的GloVe静态词嵌入模型中与青少年最相关的前1000个词汇里，超过50%反映了此类问题。在关于青少年的提示下，GPT2-XL模型输出的30%和LLaMA-2-7B模型输出的29%涉及社会问题，最常见的是暴力，其次是吸毒、精神疾病和性禁忌。尼泊尔语模型虽未完全摆脱此类关联，但社会问题的主导性较弱。通过与美国13名、尼泊尔18名青少年开展的研讨工作坊数据表明，人工智能的呈现与青少年以学业和友谊等日常活动为中心的实际生活脱节。参与者对20个特质词描述青少年贴合度的评分，与静态词嵌入模型的关联度呈现弱相关性：英语FastText模型的皮尔逊相关系数r=0.02（不显著），GloVe模型r=0.06（不显著）；尼泊尔语FastText模型r=0.06（不显著），GloVe模型r=-0.23（不显著）。美国参与者建议人工智能可通过强调多样性来公正呈现青少年，而尼泊尔参与者则更注重积极正向的描绘。参与者乐观地认为，如果人工智能能向青少年而非媒体资料学习，将有助于消解刻板印象。本研究揭示了静态词嵌入模型和生成式语言模型如何误读这一处于发展脆弱期的群体，并为减少 sensationalized 的表征提供了改进框架。