Biased or Personalized? The Impact of Personal Information on AI-driven Development

• URL: http://arxiv.org/abs/2607.07480v1
• Date: Wed, 08 Jul 2026 14:43:08 GMT
• Title: Biased or Personalized? The Impact of Personal Information on AI-driven Development
• Abstract summary: We show that developer attributes can meaningfully influence generated software beyond stated requirements.<n>Our results highlight a previously underexplored tension between personalization and fairness in AI-assisted programming.
• Score: 4.501897090965463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative AI is increasingly permeating software engineering, enabling developers to generate functions, files, and even entire applications from natural language specifications. AI systems are also becoming more personalized, adapting outputs based on inferred user characteristics and interaction history. While personalization may improve the development experience, it raises concerns that generated software could be shaped by attributes of the developer rather than by task requirements alone. Prior work has shown that generative AI can produce biased software artifacts, but little is known about how developer identity can bias generated code. We characterize three dimensions through which inferred developer attributes can influence generated artifacts: interface design, template content, and code structure. First, through controlled experiments on 800 AI-generated websites, we find that age- and gender-related signals produce significant differences across all three dimensions. Second, we conduct an observational study and follow-up interviews with 20 participants who used AI to create a personal website to both examine how personalization impacts software artifacts in practice, and also to understand how programmers perceive the boundary between personalization and bias. Together, our results show that developer attributes can meaningfully influence generated software beyond stated requirements, highlighting a previously underexplored tension between personalization and fairness in AI-assisted programming.

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