A Standardized Machine-readable Dataset Documentation Format for Responsible AI
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Jain, Nitisha Akhtar, Mubashara Giner-Miguelez, Joan Shinde, Rajat Vanschoren, Joaquin Vogler, Steffen Goswami, Sujata Rao, Yuhan Santos, Tim Oala, Luis Karamousadakis, Michalis Maskey, Manil Marcenac, Pierre Conforti, Costanza Kuchnik, Michael Aroyo, Lora Benjelloun, Omar Simperl, Elena |
| author_facet | Jain, Nitisha Akhtar, Mubashara Giner-Miguelez, Joan Shinde, Rajat Vanschoren, Joaquin Vogler, Steffen Goswami, Sujata Rao, Yuhan Santos, Tim Oala, Luis Karamousadakis, Michalis Maskey, Manil Marcenac, Pierre Conforti, Costanza Kuchnik, Michael Aroyo, Lora Benjelloun, Omar Simperl, Elena |
| contents | Data is critical to advancing AI technologies, yet its quality and documentation remain significant challenges, leading to adverse downstream effects (e.g., potential biases) in AI applications. This paper addresses these issues by introducing Croissant-RAI, a machine-readable metadata format designed to enhance the discoverability, interoperability, and trustworthiness of AI datasets. Croissant-RAI extends the Croissant metadata format and builds upon existing responsible AI (RAI) documentation frameworks, offering a standardized set of attributes and practices to facilitate community-wide adoption. Leveraging established web-publishing practices, such as Schema.org, Croissant-RAI enables dataset users to easily find and utilize RAI metadata regardless of the platform on which the datasets are published. Furthermore, it is seamlessly integrated into major data search engines, repositories, and machine learning frameworks, streamlining the reading and writing of responsible AI metadata within practitioners' existing workflows. Croissant-RAI was developed through a community-led effort. It has been designed to be adaptable to evolving documentation requirements and is supported by a Python library and a visual editor. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16883 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Standardized Machine-readable Dataset Documentation Format for Responsible AI Jain, Nitisha Akhtar, Mubashara Giner-Miguelez, Joan Shinde, Rajat Vanschoren, Joaquin Vogler, Steffen Goswami, Sujata Rao, Yuhan Santos, Tim Oala, Luis Karamousadakis, Michalis Maskey, Manil Marcenac, Pierre Conforti, Costanza Kuchnik, Michael Aroyo, Lora Benjelloun, Omar Simperl, Elena Information Retrieval Artificial Intelligence Computers and Society Databases Machine Learning Data is critical to advancing AI technologies, yet its quality and documentation remain significant challenges, leading to adverse downstream effects (e.g., potential biases) in AI applications. This paper addresses these issues by introducing Croissant-RAI, a machine-readable metadata format designed to enhance the discoverability, interoperability, and trustworthiness of AI datasets. Croissant-RAI extends the Croissant metadata format and builds upon existing responsible AI (RAI) documentation frameworks, offering a standardized set of attributes and practices to facilitate community-wide adoption. Leveraging established web-publishing practices, such as Schema.org, Croissant-RAI enables dataset users to easily find and utilize RAI metadata regardless of the platform on which the datasets are published. Furthermore, it is seamlessly integrated into major data search engines, repositories, and machine learning frameworks, streamlining the reading and writing of responsible AI metadata within practitioners' existing workflows. Croissant-RAI was developed through a community-led effort. It has been designed to be adaptable to evolving documentation requirements and is supported by a Python library and a visual editor. |
| title | A Standardized Machine-readable Dataset Documentation Format for Responsible AI |
| topic | Information Retrieval Artificial Intelligence Computers and Society Databases Machine Learning |
| url | https://arxiv.org/abs/2407.16883 |