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A 2025 investigation reported stereotypical answers from GPT-5 and recurring caste-linked stereotypes in a set of Sora images and videos. Those results describe specific prompt-based tests, not the share of all ChatGPT responses or Sora generations that are biased. Sora’s video-generation process is a separate question: OpenAI’s 2024 technical report describes a diffusion model that turns compressed visual data into video, while acknowledging that the public report omits implementation details.

What did the GPT-5 caste-bias test find?

A reproduced version of a 2025 MIT Technology Review investigation, available at Digido.ma, says the investigation worked with AI safety researcher Jay Chooi and tested GPT-5 using the Indian Bias Evaluation Dataset. The dataset contains 105 English fill-in-the-blank sentences designed to reflect stereotypes about Dalits and Brahmins.

In that test, GPT-5 selected stereotypical completions for 80 of the 105 sentences. The article summarizes this as 76%. Both figures refer to the investigators’ results on that particular set of prompts. They do not mean that 76% of ChatGPT responses are biased, or establish how often a person using the product will encounter caste bias.

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Nihar Ranjan Sahoo, a machine-learning PhD student at the Indian Institute of Technology in Mumbai, characterized caste bias as systemic in large language models trained on uncurated web-scale data. That is expert commentary about a possible source of bias, not a causal conclusion established by this prompt test. Preetam Dammu, a University of Washington PhD student studying AI robustness, fairness, and explainability, cautioned that behavior can change between runs. That variability is another reason not to treat one test’s rate as a universal prediction.

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What did the Sora caste-bias test find?

The same reported investigation says it analyzed 400 Sora-generated images and 200 videos. Its prompts examined five caste groups—Brahmin, Kshatriya, Vaishya, Shudra, and Dalit—across four themes: people, jobs, homes, and behavior.

The investigators described recurring stereotypes in depictions of occupations and homes, as well as in captions. They also reported that some initial and follow-up generations prompted with “a Dalit behavior” produced animal images. These are reported observations in the outputs they examined; they do not show that every generation, or even every prompt about a caste group, produces the same result.

The caste findings should be read with their source in view: the MIT Technology Review page was not accessible to readers, so the account here relies on a reproduced version at Digido.ma. The reported sample sizes and observations are attributed to that investigation rather than presented as an independent evaluation.

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How this differs from another Sora representation test

A separate WIRED investigation described generating 250 videos from 25 prompts to examine representation across people, jobs, relationships, and disability. It reported patterns involving job roles, disability, body type, and relationships. That work addresses a broader set of representational questions, not the same caste-focused prompts or benchmark. Its sample and interpretation cannot be combined with the caste investigation to rank performance or calculate one overall bias rate.

How are AI videos made?

OpenAI’s 2024 Sora technical report describes a text-conditioned diffusion model. In broad terms, diffusion starts with a noisy representation and repeatedly refines it toward an output that fits the conditioning information, such as a text prompt. It is not simply retrieving a finished clip from a library, and the process should not be taken to mean the model understands a scene as a human filmmaker would.

From pixels to a compressed representation

The report says Sora first compresses visual data into a lower-dimensional latent representation. Instead of working directly on every pixel throughout generation, the model works with this more compact form of the visual content.

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From visual patches to a generated sequence

Sora divides the compressed representation into spacetime patches—units that represent visual information across both space and time. A transformer is trained to predict clean patches from noisy ones. The generated representation is then passed through a decoder that maps it back into video pixels. The report says the same approach can handle different durations, resolutions, and aspect ratios; it also treats an image as a one-frame video.

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How prompts and other inputs shape the result

The 2024 report describes a captioning stage in which a model produces detailed captions for training videos. A short user prompt may also be expanded into a longer description before it reaches the video model. Sora can take images or videos as inputs for tasks such as animation, extension, and editing, according to that report.

This is a high-level description, not a full blueprint. OpenAI says the 2024 report focuses on its method for representing visual data and on qualitative evaluation of capabilities and limitations; it does not include model or implementation details.

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Why a plausible clip can still contain errors

OpenAI describes some simulation-like behavior emerging in Sora, but its 2024 report also documents shortcomings involving basic physical interactions, long samples, and whether objects persist consistently. A video can therefore look coherent in parts while mishandling how objects move or remain present over time. The published account does not establish that the system consistently models the underlying physical scene.

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What safeguards does OpenAI describe, and what do they establish?

OpenAI’s December 2024 Sora System Card describes selected publicly available, partnership, and internally created datasets, along with human feedback. The company says it filters training data for explicit, violent, or other sensitive content and describes prompt moderation, output classifiers, blocklists, provenance measures, and red-team exercises. It also identifies representation and body-image bias as ongoing work and notes that overcorrection can be harmful.

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These are disclosures about OpenAI’s stated processes, not independent proof that generated content is free from bias. A separate, later Sora 2 safety document describes safeguards and red-team assessment for that release and explicitly warns that layered safeguards may still be circumvented. Sora 2’s documentation should not be treated as a description of every feature or safeguard in the 2024 Sora release.

OpenAI reported that its 2024 early-access program involved more than 300 users in more than 60 countries and received more than 500,000 model requests. Those figures describe program activity, not a fairness evaluation or a measurement of caste bias.

What can readers reasonably conclude?

  • The reported GPT-5 result is 80 stereotypical completions among 105 test sentences in one specified investigation, not a general rate for ChatGPT.
  • The Sora findings describe patterns investigators saw in 400 images and 200 videos made for a caste-focused prompt test; the account does not show that all outputs behave that way.
  • OpenAI’s public technical explanation describes a compressed, patch-based diffusion process, while leaving implementation details unpublished.
  • OpenAI documents multiple safeguards and ongoing bias work, but those disclosures do not establish that bias is absent or that safeguards cannot fail.

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