Generative AI personas considered harmful? Putting forth twenty challenges of algorithmic user representation in human-computer interaction

• Shows how GenAI fundamentally transforms existing persona development issues through evolutionary amplification rather than creating entirely new problems, with traditional biases becoming algorithmic discrimination and manual inconsistencies becoming convincing AI hallucinations. • Reveals how traditional limitations manifest differently in GenAI contexts across transparency, fairness, reliability, and control domains, with expert validation showing 60% of challenges are more problematic for GenAIPs than conventional approaches. • Documents how GenAI transforms not just technical challenges but harm distribution, with persona developers facing operational complexity while target user groups bear severe consequences through systematic misrepresentation and exclusion. • Provides evidence that while GenAIPs appear to solve traditional limitations, they transform existing challenges into more complex forms requiring novel validation approaches and human-AI collaboration frameworks for responsible implementation. 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.