Generative AI personas considered harmful? Putting forth twenty challenges of algorithmic user representation in human-computer interaction - Amin, Danial; Salminen, Joni; Jansen, Bernard J.; Shin, Joongi; Kim, Dae Hyun (Elsevier, 2025)
Generative AI personas (GenAIPs) promise user-centred design efficiency, but their impact on different persona challenges remains unexplored. Inspired by Dijkstra’s classic essay on harmful programming constructs, we analyze twenty challenges in persona development using Human-Centered AI principles. Through literature review and expert survey (n = 17), we find that GenAIPs transform rather than eliminate traditional persona challenges. Experts rated all challenges as problematic for GenAIPs (M > 4.0), with the highest concerns for hallucinations (M = 5.94), over-sanitization (M = 5.82), and lack of standardization (M = 5.59). 12 out of 20 challenges are considered more problematic for GenAIPs than conventional personas, particularly bias amplification, validation challenges, and accessibility without expertise. We provide HCAI-grounded guidelines demonstrating that effective GenAIP implementation requires human-AI collaboration rather than automation and prioritizing user welfare over technical efficiency.
Keywords: Personas; Generative AI personas; Harmful; Human-centered AI; fi=Markkinointi|en=Marketing|; fi=Markkinointi|en=Marketing|