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arxiv: 2606.07969 · v1 · pith:ZHVBENCInew · submitted 2026-06-06 · 💻 cs.CL · cs.AI

Neutrality Bites: Gender Representation in AI-Generated Animal Stories

Imani Finkley , Yuanxi Li , Melanie Walsh This is my paper

Pith reviewed 2026-06-27 20:11 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords gender biaslarge language modelsanimal storiesneutralitymasculine biasAI-generated narrativesidentity erasure
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0 comments X

The pith

Large language models assign masculine gender to animal characters far more often than feminine when they assign gender at all.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests six leading LLMs by prompting them to complete stories about seven anthropomorphic animals whose gender is left unstated. Models often avoid assigning any gender or use neutral pronouns, but when they do specify gender the results show masculine characters in 40.6 percent of stories and feminine characters in only 2.2 percent. The pattern holds across four narrative settings and a range of temperatures. The authors conclude that efforts to achieve neutrality can produce the erasure of feminine and other marginalized identities rather than balanced representation. They argue that new approaches are needed to distribute social possibilities more evenly across imagined subjects.

Core claim

Across 23.8K stories, models avoid gendering the animal character in 19 percent of cases on average and use gender-neutral language in 38.2 percent, yet assign masculine gender in 40.6 percent and feminine gender in 2.2 percent when gender is assigned; the authors therefore claim that neutrality in LLMs contributes to the erasure of marginalized perspectives and identities.

What carries the argument

Prompting LLMs to complete English-language stories about anthropomorphic animals with unstated gender, then measuring the resulting patterns of gender assignment versus neutrality.

Load-bearing premise

The six chosen LLMs, seven animals, four settings, and temperature values are representative enough to support general claims about how LLMs assign gender in ambiguous narrative contexts.

What would settle it

A larger study using additional models or prompts that finds feminine animal characters appearing at rates equal to or higher than masculine ones when gender is assigned.

Figures

Figures reproduced from arXiv: 2606.07969 by Imani Finkley, Melanie Walsh, Yuanxi Li.

Figure 1
Figure 1. Figure 1: Example prompt and response from GPT-4o with the following parameters: bear (subject), farm (setting), and 1.0 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Gender distribution results from 1327 human survey respondents given a story-completion task with varying [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Gender assignment distribution results from 23.8K text outputs from six LLMs given a story-completion task with [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Gender assignment distribution results when gender-neutral representation is not considered, highlighting a significant [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Gender assignment distribution across narrative settings for generated stories. [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Gender assignment distribution across temperatures for generated stories. [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
read the original abstract

Gender bias in AI-generated stories is a well-documented problem. While much attention has been paid to reducing or mitigating this bias, it is not always clear whether interventions produce genuinely fairer results. To investigate this issue, we examine how large language models (LLMs) handle gender assignment in a narrative context that is popular, highly ambiguous, and also known to closely reproduce human stereotypes: stories about talking animals. We prompt six leading LLMs to complete an English-language story about seven different anthropomorphic animal characters whose gender is unstated. We additionally iterate with four different narrative settings and a range of model temperatures. Across the 23.8K stories, we find that models frequently avoid gendering the animal character in the story (19% on average) or use gender-neutral language like "it" or "its" (38.2% on average). However, when gender is assigned, there is a significant masculine bias. Feminine animal characters are virtually absent, present in just 2.2% of stories vs. 40.6% that feature masculine characters. Our findings point to a broader argument: neutrality bites. In other words, models that prioritize neutrality to address social bias may actually contribute to the erasure of marginalized perspectives and identities. We suggest that alternative strategies beyond neutrality need to be pursued, such as ones that more equally distribute social possibilities across imagined subjects.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper claims that LLMs prompted to generate stories about seven anthropomorphic animals with unstated gender (across six models, four narrative settings, and varied temperatures, yielding 23.8K stories) frequently avoid explicit gendering (19% average) or use neutral pronouns (38.2% average). When gender is assigned, a strong masculine bias appears (40.6% masculine characters vs. 2.2% feminine). The authors argue this shows that neutrality-focused approaches can erase marginalized identities and call for alternative strategies.

Significance. If the gender classification procedure proves reliable, the scale of the empirical counts provides concrete evidence that LLMs default to masculine or neutral representations in ambiguous narrative contexts, supporting the broader claim that neutrality can contribute to underrepresentation. The direct generation of 23.8K stories without fitted parameters is a methodological strength for reproducibility.

major comments (3)
  1. [Methodology] Methodology section: The procedure for classifying generated stories as masculine, feminine, neutral, or ungendered is not described, including any annotation guidelines, inter-annotator agreement scores, validation against human judgments, or handling of ambiguous cases. This directly affects the reliability of the headline 2.2% and 40.6% figures.
  2. [Results] Results section: No per-model, per-animal, or per-setting breakdowns or variance measures are reported for the gender assignment rates. Without these, it is impossible to determine whether the masculine bias generalizes beyond the specific choice of six LLMs and seven animals or is driven by particular combinations.
  3. [Abstract] Abstract and Results: The reported percentages lack error bars, confidence intervals, or robustness checks across temperature values, leaving the central claim of 'significant masculine bias' vulnerable to unexamined measurement and sampling choices.
minor comments (1)
  1. [Abstract] The abstract states the total story count but provides no details on the exact prompting template or how stories were deduplicated or filtered.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important areas for improving the clarity and robustness of our manuscript. We address each major comment below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

  1. Referee: [Methodology] Methodology section: The procedure for classifying generated stories as masculine, feminine, neutral, or ungendered is not described, including any annotation guidelines, inter-annotator agreement scores, validation against human judgments, or handling of ambiguous cases. This directly affects the reliability of the headline 2.2% and 40.6% figures.

    Authors: We agree that the classification procedure must be described in detail for the results to be interpretable. The current version of the manuscript does not include this information. In the revision, we will add a dedicated subsection to the methodology that specifies the exact rules for classifying stories (based on pronoun usage and explicit gender markers), how ambiguous cases were resolved, the annotation guidelines provided to any human coders, inter-annotator agreement statistics, and the results of a validation exercise against human judgments on a held-out sample. revision: yes

  2. Referee: [Results] Results section: No per-model, per-animal, or per-setting breakdowns or variance measures are reported for the gender assignment rates. Without these, it is impossible to determine whether the masculine bias generalizes beyond the specific choice of six LLMs and seven animals or is driven by particular combinations.

    Authors: The referee is correct that aggregate figures alone are insufficient to assess generalizability. We will revise the results section to include tables or supplementary figures with per-model, per-animal, and per-narrative-setting breakdowns of the gender assignment rates. We will also report variance measures (e.g., standard deviations across temperature settings) to allow readers to evaluate consistency. revision: yes

  3. Referee: [Abstract] Abstract and Results: The reported percentages lack error bars, confidence intervals, or robustness checks across temperature values, leaving the central claim of 'significant masculine bias' vulnerable to unexamined measurement and sampling choices.

    Authors: We accept this criticism. The revised manuscript will include error bars or confidence intervals for the key percentages in both the abstract and results. We will also add a short robustness subsection that reports gender assignment rates across the range of temperature values used, confirming that the masculine bias pattern is stable. revision: yes

Circularity Check

0 steps flagged

No circularity: direct empirical counts from generated text

full rationale

The paper performs an empirical study by prompting six LLMs to generate stories about seven animals in four settings, then manually or automatically classifying the 23.8K outputs for gender assignment (masculine, feminine, neutral, or avoided). The headline percentages (40.6% masculine, 2.2% feminine when gendered) are simple frequency counts of observed tokens and pronouns in the generated text; no equations, fitted parameters, predictions, or self-citations are used to derive these figures from the inputs. The central claim is therefore a direct report of the experimental data rather than a reduction of the data to itself by construction. The selection of models/animals/settings is a methodological choice whose representativeness can be debated on external grounds, but it does not create a circular derivation chain.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim depends on the representativeness of the selected models and prompts plus the validity of automated or manual gender classification from generated text; no free parameters are fitted to the outcome data.

free parameters (2)
  • temperature values
    Chosen by hand to explore generation diversity; not fitted to the gender statistics.
  • selection of seven animal characters
    Chosen to ensure gender ambiguity; specific choice could influence observed bias rates.
axioms (2)
  • domain assumption LLMs produce text from which gender references can be reliably extracted and counted
    Required to convert raw generations into the reported 2.2% and 40.6% figures.
  • domain assumption The tested models and settings are representative of typical LLM narrative behavior
    Invoked when generalizing from the 23.8K stories to broader claims about AI gender handling.

pith-pipeline@v0.9.1-grok · 5777 in / 1299 out tokens · 34875 ms · 2026-06-27T20:11:20.819339+00:00 · methodology

discussion (0)

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