[{"data":1,"prerenderedAt":544},["ShallowReactive",2],{"item:\u002Fai-generated-content\u002Fbias-quality-and-representational-harms-in-generative-models-evaluation-and-miti":3,"platform:site":149,"more:\u002Fai-generated-content\u002Fbias-quality-and-representational-harms-in-generative-models-evaluation-and-miti":499},{"id":4,"title":5,"aiRevisedAt":6,"authors":7,"body":12,"canonical":79,"category":80,"categorySlugs":91,"cid":92,"cover":93,"description":97,"extension":98,"featured":99,"language":100,"meta":101,"navigation":102,"noindex":99,"ogImage":103,"path":104,"publishedAt":105,"readingMinutes":71,"recipe":79,"related":106,"revisedAt":79,"seo":137,"seoDescription":97,"seoTitle":5,"sortKey":79,"sources":138,"stem":142,"tagSlugs":143,"tags":144,"translations":145,"type":146,"url":147,"__hash__":148},"platformItems\u002Fplatform\u002Fitems\u002F86323.md","Building Fair Generative AI: Tracing Bias Origins and Applying Layered Fixes","2026-10-21T06:59:42.000Z",[8],{"name":9,"slug":10,"role":11},"Drew Wood","drew-wood","author",{"type":13,"value":14,"toc":69},"minimark",[15,20,24,28,31,34,38,46,49,52,56,59,62,66],[16,17,19],"h3",{"id":18},"introduction","Introduction",[21,22,23],"p",{},"Generative models have spread quickly from experimental settings into routine use, churning out text, imagery, and other media at scale. 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Text generators make similar profession-gender associations, pairing \"engineer\" with men and \"secretary\" with women.",[21,32,33],{},"Several forces drive these outcomes: the training data, the model architecture, and the optimization targets. Datasets that are imbalanced and underrepresent certain groups yield skewed outputs. Language models pretrained on biased corpora reinforce stereotypes through statistical associations. Optimization objectives, even when intended to be benign, can unintentionally entrench bias when they are not designed with care.",[16,35,37],{"id":36},"_2-assessing-factuality-toxicity-and-stereotyping","2. Assessing Factuality, Toxicity, and Stereotyping",[21,39,40],{},[41,42],"img",{"alt":5,"height":43,"src":44,"width":45},899,"\u002Fmedia\u002F30169f6b57dddf42f304.webp",1600,[21,47,48],{},"Gauging the quality and harms of generative output calls for an approach with several dimensions. Factuality work centers on whether generated text is truthful. 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