Know Your Author: Does the AI Penalty Hold in Short Fiction?
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911734288089088 |
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| author | Todasco, Michael Cesare, Joselyn |
| author_facet | Todasco, Michael Cesare, Joselyn |
| contents | Public concern about an "AI penalty" suggests that labeling content as AI-generated may negatively influence how it is evaluated. We tested this claim in a preregistered experiment (N = 254, per protocol) using a pure attribution design: participants read one of two ~200-word vignettes and were randomly assigned to see it labeled as Human-written, AI-written, or presented with no author line. Authorship labels did not produce reliable main effects on creativity, enjoyment, recommendation, or originality; observed effect sizes were uniformly small. However, labels strongly influenced inferred effort: participants estimated that Human-labeled stories took far longer to create than AI-labeled stories (back-transformed geometric means from ln[minutes + 1]: 148 vs. 6 minutes). Across conditions, higher inferred effort predicted greater enjoyment, and this relationship was also present within the AI-labeled condition. Additionally, participants' prior attitudes toward AI moderated recommendation judgments: more positive attitudes were associated with higher recommendation ratings for AI-labeled stories, but not for Human-labeled stories. These findings suggest that while AI authorship labels do not systematically alter average evaluations of short fiction, they meaningfully shape perceptions of effort and interact with prior beliefs to influence downstream judgments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2606_00006 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Know Your Author: Does the AI Penalty Hold in Short Fiction? Todasco, Michael Cesare, Joselyn Human-Computer Interaction Computers and Society I.2.7; H.5.2 Public concern about an "AI penalty" suggests that labeling content as AI-generated may negatively influence how it is evaluated. We tested this claim in a preregistered experiment (N = 254, per protocol) using a pure attribution design: participants read one of two ~200-word vignettes and were randomly assigned to see it labeled as Human-written, AI-written, or presented with no author line. Authorship labels did not produce reliable main effects on creativity, enjoyment, recommendation, or originality; observed effect sizes were uniformly small. However, labels strongly influenced inferred effort: participants estimated that Human-labeled stories took far longer to create than AI-labeled stories (back-transformed geometric means from ln[minutes + 1]: 148 vs. 6 minutes). Across conditions, higher inferred effort predicted greater enjoyment, and this relationship was also present within the AI-labeled condition. Additionally, participants' prior attitudes toward AI moderated recommendation judgments: more positive attitudes were associated with higher recommendation ratings for AI-labeled stories, but not for Human-labeled stories. These findings suggest that while AI authorship labels do not systematically alter average evaluations of short fiction, they meaningfully shape perceptions of effort and interact with prior beliefs to influence downstream judgments. |
| title | Know Your Author: Does the AI Penalty Hold in Short Fiction? |
| topic | Human-Computer Interaction Computers and Society I.2.7; H.5.2 |
| url | https://arxiv.org/abs/2606.00006 |