BIAS IN LLMS, OR HOW TO DESIGN FOR EVERYONE WITH TOOLS BUILT FOR SOMEONE

Summary from the TLDR Design newsletter

Large language models are not neutral and often reflect or amplify biases related to language, geography, gender, culture, and socioeconomic status because of the data they are trained on and the feedback systems used to refine them. As a result, AI-generated personas, research summaries, copy, and recommendations can overrepresent dominant groups and present their perspectives as consensus while overlooking minority viewpoints. Designers should treat AI outputs as inputs for further thinking rather than objective conclusions, and actively review them for hidden assumptions, missing perspectives, and demographic bias.