This dataset was developed through a research collaboration project aimed at reducing the AI divide for marginalized communities by improving the representation of people with disabilities in text-to-image model outputs. The dataset focuses on the short stature community, featuring 400 real-world images with corresponding metadata. The images highlight individuals and groups, reflecting key representation themes and subthemes defined by the community as “good representation.” They also capture diversity (such as categories of dwarfism), different age groups (such as children, adults), a range of activities (such as sports, work), and varied settings (such as school, home, farms). Collected in Kenya, the dataset was curated between April and July 2025.

Supported Tasks

Text-to-Image: The dataset can be used to train, evaluate or modify text-to-image generation models.

Languages

The annotations within each image are in English. The associated BCP-47 code is en.

Dataset Structure

Data Instances

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Data Fields

The metadata includes: • A hierarchy of themes (with name and description) and subthemes (title, name, description) that reflect desired representation aspirations of the community across the entirety of the 400-image dataset. • A rationale for “why” an image has been selected as a good instance of representation theme (description). • A prompt describing the image (prompt); and (4) 1-5 image text (label) and bounding-box annotations via x, y, w, h dimensions.

Dataset Creation

Curation Rationale

This dataset was curated to support the adaptation and evaluation of text-to-image generative models. It is organized around five core themes, chosen through participatory input from the community to capture meaningful activities, characteristics, and objects for AI generated imagery. Each theme is sub-divided into hierarchical sub-themes, offering a structured taxonomy. Within each sub-theme, curated images and detailed annotations illustrate the visual and semantic traits of the category. This structure enables targeted evaluation of model performance across conceptual domains and facilitates research on theme-specific generation fidelity, compositional generalization, and prompt grounding.

Source Data

Initial Data Collection

The dataset was collected between June and July 2025 using a participatory, community-driven approach. Participants were persons of short stature, selected to ensure diversity in age, gender, categories of dwarfism, and social roles. Images were captured in various real-world settings including homes, schools, workplaces, farms, sports fields and community events across multiple Kenyan regions. Geographical and contextual variety ensured broad representation. The review process prioritized image quality, clarity, relevance to pre-defined themes, and accurate depictions of activities, objects, and characteristics valued by the community. Final inclusion emphasized diversity, cultural authenticity, and meaningful representation within each thematic category.

Who are the source data producers?

The dataset was produced entirely by humans. The images were originally created by local photographers and community volunteers who collaborated with persons of short stature to document authentic representations. Demographic information about the image creators is not recorded and remains unknown. Data was generated through two primary approaches: direct image capture by photographers and voluntary data donations from community members who contributed personal or family photographs. All contributors participated willingly, with informed consent obtained before inclusion. To recognize their time and effort, photographers and community members who provided images or assisted in curation received fair monetary compensation.

Annotations

Annotation process

Dataset-level annotations To structure their image library, annotators grouped and categorized images into 5 themes (e.g., Work, Relationships, Sports, Diversity and Education/Hobbies) with 3 sub-themes (e.g., formal profession, informal profession, family relationship, romantic relationship, competitive single person sport, competitive team sport e.t.c). Individual image-level annotations Instructions for the creation of the annotations for each image involved the annotators’ response to the following questions/ tasks: Why did you select this image as a good representation for your community? Edits to an auto-generated prompt to ensure its accuracy in regards to the image and in describing what is important for the community using their preferred language. A set of 1-5 bounding box annotations that either relate to: (i) Objects that are special or specific to the community (e.g., Adapted car); or (ii) People and/or animals that are important for your community (e.g., Young Hispanic woman who has low vision; Service dog wearing an orange vest).

Who are the annotators?

All annotations were completed by a single individual who served as the project lead for the dataset generation process. No demographic or identity information about the annotator is provided and therefore remains unknown. Annotations were created under controlled conditions to ensure consistency, aligning with the predefined themes and subthemes that guided dataset organization. The annotator reviewed each image for quality, thematic relevance, and representation before assigning metadata. As part of the project’s ethical framework, fair monetary compensation was provided for the time and expertise dedicated to the annotation process.

Personal and Sensitive Information

The dataset is not explicitly linked to individuals, and no personal identifiers are included and therefore subjects cannot be directly identified. However, images may reveal sensitive information such as racial and ethnic origins, cultural backgrounds, or visible health related characteristics associated with specific categories of dwarfism. The dataset also contains images of children participating in various activities. While all images were collected with informed consent, users should be aware of the potential for indirect sensitivity in certain contexts and handle the dataset with appropriate ethical considerations and privacy safeguards.

Considerations for Using the Data

Social Impact of Dataset

The dataset has the potential to create significant positive social impact by improving the representation of persons of short stature in AI-generated imagery, fostering inclusion, and reducing the AI divide for marginalized communities. The dataset can support the development of technologies that generate fairer, more respectful, and diverse depictions, contributing to greater social awareness and positive cultural narratives. Such advancements may enhance accessibility, representation in media, and equitable participation in emerging AI-driven industries. However, its use also carries risks. Misuse of dataset could enable the creation of stigmatizing, defamatory, or pornographic content that harms individuals or the community it represents. It may also be exploited for surveillance, discriminatory profiling, or the reinforcement of harmful stereotypes. Despite the dataset not containing explicit personal identifiers, privacy risks remain, including potential re-identification of individuals. To mitigate harm, users must adhere to ethical guidelines, prioritize responsible use, and ensure outputs do not perpetuate discrimination or bias.

Discussion of Biases

The dataset may reflect inherent biases due to its scope and collection context. It primarily features persons of short stature from Kenya, limiting geographic, cultural, and ethnic diversity. Certain themes, sub-themes, settings, or activities – such as experiences of children, the elderly, or individuals in less-documented environments – maybe underrepresented. Additionally, the dataset focuses exclusively on one disability community rather than encompassing a broader spectrum of disabilities, which may narrow its generalizability. To reduce these impacts, participatory input guided theme selection, and efforts were made to include diverse age groups, activities and settings. Future expansions could address these gaps by incorporating additional regions, broader disability categories, and more varied life contexts to enhance representational balance. Limitations: The dataset is relatively small in scale and may not capture the full diversity of lived experiences. Its focus on Kenyan contexts limits global applicability. It is intended primarily for research and model evaluation, not as a definitive or exhaustive representation of all persons of short stature.

Additional Information

Dataset Curators

The dataset was curated by Ruth Mueni and Short Stature Society of Kenya in collaboration with Microsoft Research. This partnership combined community led insights with technical expertise to ensure authentic representation, ethical data handling, and alignment with research goals focused on improving inclusivity in text-to-image generative models.

Licensing Information

This dataset is licensed under the Creative Commons Attribution-ShareAlike 4.0 license.

Contributions

We extend heartfelt gratitude to the community members whose participation made this dataset possible. Special appreciation goes to Mike Odera of the Short Stature Society of Kenya for his invaluable support in the dataset curation/open-sourcing efforts, helping ensure the dataset reflects the lived experiences and voices of the community as well as their authentic representation and meaningful contributions to inclusive AI research.

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