# AI Data & Human Feedback

> The data layer of AI development: labeling and annotation platforms like Label Studio and Scale AI, human feedback and preference tooling like Argilla and Snorkel, dataset quality checks with Cleanlab, and dataset management.

8 tools in 4 subcategories. Web page: https://ailandscape.org/category/ai-data-human-feedback · Each tool also has a markdown version at /tool/{slug}.md

## Data Labeling & Annotation

- [Label Studio](https://ailandscape.org/tool/label-studio): Open-source data labeling platform for all data types
- [Labelbox](https://ailandscape.org/tool/labelbox): Data factory for labeling, RL environments, and preference signals
- [Prodigy](https://ailandscape.org/tool/prodigy): Scriptable annotation tool by the makers of spaCy
- [Scale AI](https://ailandscape.org/tool/scale-ai): Data labeling and human feedback platform for frontier AI training

## Human Feedback & Preference Data

- [Argilla](https://ailandscape.org/tool/argilla): Open-source platform for AI feedback, preference data, and curation
- [Snorkel](https://ailandscape.org/tool/snorkel): Programmatic data labeling and AI data development platform

## Data Quality & Curation

- [Cleanlab](https://ailandscape.org/tool/cleanlab): Detect and fix label errors and data issues in ML datasets

## Dataset Management

- [Hugging Face Datasets](https://ailandscape.org/tool/hugging-face-datasets): Library and hub for sharing and processing ML datasets

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Part of [AI Landscape](https://ailandscape.org), an open map of the AI ecosystem. Index for AI assistants: https://ailandscape.org/llms.txt