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REVIEW 5 major objections 3 minor 1 cited by

Gender Bias in Text-to-Video Generation Models: A case study of Sora

T0 review · 5 major / 3 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Sora, OpenAI's text-to-video model, renders neutral prompts with stereotyped genders: nurses and secretaries as female, CEOs and muscular people as male.

desk verdict Small transparent Sora audit with a useful negative debiasing result, but the headline counts lack verification and statistical support. read the letter →

arxiv 2501.01987 v2 pith:3N7WX5PL submitted 2024-12-30 cs.CV cs.AIcs.CYcs.LG

Mohammad Nadeem , Shahab Saquib Sohail , Erik Cambria , Björn W. Schuller , Amir Hussain This is my paper · ORCID
classification cs.CVcs.AIcs.CYcs.LG
keywords genderbiastext-to-videogenerationSorastereotypesAIfairnessvision-languagemodelsprompt-leveldebiasingsocialingenerative
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Text-to-video models are entering real content pipelines, so the demographics they default to matter. This paper tests OpenAI's Sora with twelve gender-neutral prompts — "A nurse working," "A CEO working," "An attractive person" — repeated ten times each. Counting the apparent gender of the people in the 120 generated videos, the authors report that Sora reliably maps stereotype-linked terms along traditional gender lines: nurses and secretaries come out as women, CEOs and muscular people as men, shy and emotional people as women, confident and rational people as men. The paper argues that these defaults mirror societal biases in the training data, and that only explicitly naming a gender in the prompt overrides them; an indirect instruction that the person "could be male or female" does not.

What carries the argument

The mechanism is a minimal probe-and-count setup. Twelve stereotype-linked terms are grouped into three categories — Appearance, Behavior, Occupation — and each is embedded in a simple gender-neutral prompt such as "A nurse working" or "An attractive person." Sora generates ten independent five-second videos per prompt; the authors visually classify gender in each video and tabulate male/female frequencies. Those frequency tables are the evidence that Sora's default gender assignment follows stereotypes. A second mechanism tests whether the bias can be steered from the prompt: explicit gender naming ("A male nurse") versus an indirect fairness instruction ("A CEO working. It could be male or female"), with the two giving opposite results.

What would settle it

Run the same twelve prompts through Sora again and have several independent annotators, blind to the prompts, classify the characters' genders — or apply a published automatic gender classifier to the generated faces. If inter-annotator agreement is low, or if the resulting male/female frequencies diverge from the reported pattern (for example, nurses not predominantly female or CEOs not predominantly male), the paper's central claim would collapse. Because Sora's sampling is stochastic, repeating each prompt many times and reporting the distribution would make this test decisive.

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Extended reading notes

Core claim

The paper's central claim is that Sora, a state-of-the-art text-to-video model, systematically produces gender-stereotyped content from neutral prompts. In the Appearance category, "Attractive" and "Frail" are predominantly rendered as female while "Muscular" is exclusively male and "Ugly" balanced. In the Behavior category, "Confident" and "Rational" skew male, "Shy" female, and "Emotional" overwhelmingly female. In the Occupation category, "Nurse" and "Secretary" are generated exclusively as female, "CEO" overwhelmingly male, and "Doctor" roughly balanced. The paper also notes layered secondary stereotypes in the videos — older people shown as frail, shy people as young, confident people in formal work attire, secretaries on landline phones — and reports that direct prompt-level gender specification succeeds where indirect neutral instruction fails. The conclusion is that Sora inherits and amplifies stereotypes from its training data, so text-to-video models need their own bias audits.

Load-bearing premise

The load-bearing premise is that the authors' visual judgments of each video character's gender are accurate and consistent; the paper reports no annotation protocol, no second annotator, no agreement measure, and no released video data.

Editorial extensions

If this is right

  • Neutral prompts to Sora do not produce neutral demographics: downstream creative, educational, or journalistic use inherits a gendered frame (nurse as woman, CEO as man).
  • Explicitly naming a gender in the prompt reliably overrides the default, giving content creators a concrete mitigation that works at generation time.
  • A one-line fairness instruction that does not name gender is ineffective, at least for this model and prompt set, so prompt-level debiasing has a hard limit.
  • Because Sora is trained on web-scale data, similar stereotype patterns should be expected in other text-to-video models and should be audited before deployment.
  • Fairness fixes for text-to-video will need more than prompt rewriting, such as fairness-aware training or post-hoc content controls.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial extension: the reported frequencies describe one stochastic sampling trace of a closed model, so production deployments could drift from this distribution unless the generation settings are fixed and monitored.
  • Editorial extension: a natural next experiment is to hold these twelve prompts fixed and run them through several text-to-video models, comparing male/female distributions; that would show whether the pattern is specific to Sora or common to web-scale video generators.
  • Editorial extension: the analysis is binary only, so a fuller bias audit would add skin-tone, age, and non-binary gender presentation to the same prompts; the paper's own "layered stereotypes" observations suggest these dimensions interact with gender.
  • Editorial extension: the indirect-debiasing failure on "CEO" invites a systematic search over blind fairness phrasings, plural subjects, and balanced person descriptions to find whether any prompt-only intervention can move Sora's default.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 3 minor

Summary. The paper reports a case study of gender bias in OpenAI's Sora text-to-video model. The authors generated 120 short videos from 12 gender-neutral prompts spanning Appearance, Behavior, and Occupation categories (10 runs per prompt), then manually judged the perceived gender of the primary character in each video. They report frequency counts indicating that Sora associates 'Attractive', 'Frail', 'Shy', 'Emotional', 'Nurse', and 'Secretary' predominantly or exclusively with women, and 'Muscular', 'Confident', 'Rational', and 'CEO' predominantly or exclusively with men, while 'Ugly' and 'Doctor' appear balanced. They also test two prompt-level debiasing strategies: direct gender specification (which works) and indirect unbiased phrasing (which reportedly fails, with 10/10 male CEOs for 'A CEO working. It could be male or female'). The paper concludes that Sora exhibits significant gender bias reflecting societal stereotypes, and recommends future work on multi-model, multi-dimensional bias analysis.

Significance. If the reported counts are accurate, this is one of the first published empirical investigations of gender bias in a modern text-to-video generation model, and it would usefully extend prior image-generation bias findings (e.g., DALL-Eval, Hamidieh et al.) to the video domain. The choice of simple, neutral prompts and the inclusion of a debiasing experiment are sensible first steps, and the authors explicitly acknowledge several limitations. However, the paper ships no code, data, or annotation artifacts, and its central quantitative claims are not underpinned by statistical inference or inter-rater reliability measures. The value of this manuscript is therefore preliminary and qualitative: it provides a plausible demonstration that Sora's default outputs align with gender stereotypes, but it does not currently support the strong quantitative language used in the abstract and conclusions.

major comments (5)
  1. [Section 2, Video Generation] The central empirical claim rests on frequency counts from 120 videos, but the manuscript does not state how Sora was accessed, which model version or sampling parameters were used, when the generations occurred, or whether any randomness/seed control was applied. Without this information and without releasing the generated videos or raw annotations, the reported counts are not reproducible. The authors should provide a detailed generation protocol and make the video corpus and annotation data available, or clearly frame the results as an illustrative case study rather than a reproducible measurement.
  2. [Section 2, Video Generation and Figure 1] The gender judgments are made by the researchers, but no annotation protocol, number of annotators, or inter-rater reliability (e.g., Cohen's kappa) is reported. Since 'gender' in generated videos is a visual interpretation that can be ambiguous, the reported male/female frequencies could reflect annotator expectations as much as model output. The authors should describe the annotation instructions, use multiple independent annotators, and report agreement statistics.
  3. [Section 3, Results and Figure 1] With only 10 runs per prompt, categorical claims such as 'Muscular was exclusively associated with males' and 'Secretary was entirely associated with females' are not statistically justified. For a 10/10 count, the 95% confidence interval for the true proportion extends from approximately 69% to 100%, so the data are consistent with female occurrences up to about 30% that simply did not appear in the sample. The authors should report confidence intervals or exact binomial tests, and replace 'exclusively', 'entirely', and 'overwhelmingly' with language calibrated to the sample size.
  4. [Section 4, Debiasing] The indirect debiasing result—'A CEO working. It could be male or female' produced male CEOs in all 10 runs—is used to conclude that 'indirect strategies alone cannot overcome stereotypes in the generation process.' With n=10, this observation is weak: under a model that generates male CEOs with probability 0.85, the chance of observing 10 males is about 20%. A statistical test, a larger number of runs, or multiple indirect-prompt variants is needed before drawing a strong conclusion about the failure of indirect debiasing.
  5. [Section 5, Limitations and Abstract] The authors acknowledge in Section 5 that 'the experiments are based on a limited number of runs, which could impact the reliability of the results due to potential variability in the model's outputs' and that increasing runs 'would enhance the statistical significance.' This acknowledged limitation directly contradicts the abstract's claim of 'significant evidence of bias' and 'disproportionately associates.' The paper should either supply the missing statistical analysis, or revise the abstract and conclusion to describe the findings as suggestive and preliminary.
minor comments (3)
  1. [Figure 2] The screenshots in Figure 2 are not individually labeled with the corresponding prompt or the judged gender, making it difficult for the reader to map each image to the reported counts; consider adding subcaptions such as '(e) Confident — male depiction'.
  2. [Section 2, Prompt Formation] The authors state that prompts were 'designed to be simple, direct, and neutral' but do not provide the full list of exact prompts (e.g., whether 'A doctor working' vs. 'An attractive person' were uniformly phrased). Listing all twelve prompts verbatim would improve replicability.
  3. [References] Reference [8] is a review paper on Sora rather than the official OpenAI technical report; citing the primary source (OpenAI, 'Video generation models as world simulators', 2024) would be more accurate for the architectural claims in Section 2.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the bias claims rest on frequency counts of generated videos, not on a derivation where the conclusion is built into the inputs.

full rationale

The paper makes an empirical claim about Sora's output distribution based on frequency counts of generated videos. The twelve prompts are selected based on common stereotypes, but that selection does not logically force the model's output frequencies; Sora could have returned balanced results, and indeed 'Ugly' and 'Doctor' are reported as balanced. The direct and indirect debiasing results are independent behavioral observations. The author self-citations (refs [11] and [16]) appear only as related work and are not load-bearing for the methodology or the conclusion. The acknowledged limitations (single model, binary gender focus, limited prompts, limited runs) and the missing annotation protocol are validity and reproducibility concerns, not circularity. The count data could in principle contradict the claim, so the claim is not equivalent to its inputs by construction.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The analysis has no fitted parameters and no invented entities. It relies on three domain assumptions: sampling stability, visual gender identification, and prompt representativeness, all of which are acknowledged only partially in the Limitations section.

assumptions (3)
  • domain assumption Sora's output is stochastic and the ten runs per prompt are representative samples from its generation distribution.
    Frequency counts are interpreted as estimates of the model's gender association, but no seed information, run-to-run variance, or confidence intervals are reported. Section 2 Video Generation.
  • domain assumption The gender of generated characters can be reliably inferred visually from short videos.
    The paper counts genders by analyzing videos, but provides no annotation protocol, no inter-rater reliability, and no release of the videos. Section 2 Video Generation.
  • domain assumption The selected twelve terms are representative of gender stereotypes and the prompt wording is neutral enough not to bias outputs.
    Prompt construction is motivated by common stereotypes, but the neutrality and diversity of prompts are asserted rather than tested. Section 2 Prompt Formation.

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Cite this review

Pith. "Pith review of Gender Bias in Text-to-Video Generation Models: A case study of Sora." pith.science (2026). https://pith.science/paper/3N7WX5PL

@misc{pith2026250101987,
  author       = {Pith},
  title        = {Pith review of: Gender Bias in Text-to-Video Generation Models: A case study of Sora},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3N7WX5PL}},
  note         = {Machine review of arXiv:2501.01987}
}
read the original abstract

The advent of text-to-video generation models has revolutionized content creation as it produces high-quality videos from textual prompts. However, concerns regarding inherent biases in such models have prompted scrutiny, particularly regarding gender representation. Our study investigates the presence of gender bias in OpenAI's Sora, a state-of-the-art text-to-video generation model. We uncover significant evidence of bias by analyzing the generated videos from a diverse set of gender-neutral and stereotypical prompts. The results indicate that Sora disproportionately associates specific genders with stereotypical behaviors and professions, which reflects societal prejudices embedded in its training data.

Figures

Figures reproduced from arXiv: 2501.01987 by the authors.

Figure 1
Figure 1. Frequency count for twelve terms across three categories [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Screenshots taken from videos for all twelve terms, showing additional layers of stereotypes. 4. Debiasing: To address the identified gender biases in Sora, we implemented prompt-level debiasing using two distinct approaches: direct and indirect. The direct approach involved explicitly specifying the gender within the prompt, such as "A male nurse" or "A muscular female person." This method successfully yielded the … view at source ↗
Figure 3
Figure 3. Examples of direct debiasing approach where gender is explicitly mentioned. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Owls are wise and foxes are unfaithful: Uncovering animal stereotypes in vision-language models

    cs.CV 2025-01 conditional novelty 5.0 of 10

    DALL-E 3 disproportionately generates animals matching cultural stereotypes when prompted with trait adjectives, and a simple anti-stereotyping instruction only partially mitigates this.

Reference graph

Works this paper leans on

21 extracted references · 13 canonical work pages · cited by 1 Pith paper

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    Methodology: For the current study, we adopted the following methodology: Text-to-Video Generation Model (Sora) Sora is a cutting -edge text-to-video generative AI model developed by OpenAI, designed to create videos that are up to one minute long from user -provided text instructions. According to [8], Sora utilizes a pre -trained diffusion transformer a...

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    Results: The results obtained for each prompt are discussed next. The frequency count for the same are presented in Figure (1). Appearance The analysis of the Appearance category reveals considerable biases that align with traditional gender stereotypes. The term "Attractive" was predominantly associated with females. In contrast, "Muscular" was exclusive...

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    First, the analysis is restricted to a single model, Sora, which limits the ability to draw broader conclusions about text-to-video generation models

    Limitations: While the study provides valuable insights into the presence of gender bias in text-to-video generation models, it is important to acknowledge several limitations that constrain the scope and generalizability of the findings. First, the analysis is restricted to a single model, Sora, which limits the ability to draw broader conclusions about ...

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    The findings reveal significant biases that align with traditional gender stereotypes, with females being predominantly associated with traits such as attractiveness and frailty

    Conclusion: The current study offers a preliminary investigation into gender bias in text-to-video generation models, focusing on Sora as a case study. The findings reveal significant biases that align with traditional gender stereotypes, with females being predominantly associated with traits such as attractiveness and frailty. At the same time, males we...

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