Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909729615249408 |
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| author | Virk, Yuvraj Liu, Dongyu |
| author_facet | Virk, Yuvraj Liu, Dongyu |
| contents | Non-technical end-users increasingly rely on AI code generation to perform technical tasks like data analysis. However, large language models (LLMs) remain unreliable, and it is unclear whether end-users can effectively identify model errors $\unicode{x2014}$ especially in realistic and domain-specific scenarios. We surveyed marketing and sales professionals to assess their ability to critically evaluate LLM-generated analyses of marketing data. Participants were shown natural language explanations of the AI's code, repeatedly informed the AI often makes mistakes, and explicitly prompted to identify them. Yet, participants frequently failed to detect critical flaws that could compromise decision-making, many of which required no technical knowledge to recognize. To investigate why, we reformatted AI responses into clearly delineated steps and provided alternative approaches for each decision to support critical evaluation. While these changes had a positive effect, participants often struggled to reason through the AI's steps and alternatives. Our findings suggest that business professionals cannot reliably verify AI-generated data analyses on their own and explore reasons why to inform future designs. As non-programmers adopt code-generating AI for technical tasks, unreliable AI and insufficient human oversight poses risks of unsafe or low-quality decisions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06484 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Virk, Yuvraj Liu, Dongyu Human-Computer Interaction Non-technical end-users increasingly rely on AI code generation to perform technical tasks like data analysis. However, large language models (LLMs) remain unreliable, and it is unclear whether end-users can effectively identify model errors $\unicode{x2014}$ especially in realistic and domain-specific scenarios. We surveyed marketing and sales professionals to assess their ability to critically evaluate LLM-generated analyses of marketing data. Participants were shown natural language explanations of the AI's code, repeatedly informed the AI often makes mistakes, and explicitly prompted to identify them. Yet, participants frequently failed to detect critical flaws that could compromise decision-making, many of which required no technical knowledge to recognize. To investigate why, we reformatted AI responses into clearly delineated steps and provided alternative approaches for each decision to support critical evaluation. While these changes had a positive effect, participants often struggled to reason through the AI's steps and alternatives. Our findings suggest that business professionals cannot reliably verify AI-generated data analyses on their own and explore reasons why to inform future designs. As non-programmers adopt code-generating AI for technical tasks, unreliable AI and insufficient human oversight poses risks of unsafe or low-quality decisions. |
| title | Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.06484 |