# Welcome diverse approaches to training data within a unified Open Source AI Definition

**URL:** <https://discuss.opensource.org/t/welcome-diverse-approaches-to-training-data-within-a-unified-open-source-ai-definition/531>\
**Category:** Open Source AI\
**Created:** [August 30, 2024, 5:24pm UTC](https://discuss.opensource.org/t/welcome-diverse-approaches-to-training-data-within-a-unified-open-source-ai-definition/531 "2024-08-30T17:24:57Z")\
**Posts on this page:** 1\
**Showing post:** 4

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**Author:** ![nick](https://yyz1.discourse-cdn.com/flex011/user_avatar/discuss.opensource.org/nick/32/542_2.png) [@nick](https://discuss.opensource.org/u/nick)\
**Post date:** [August 30, 2024, 9:39pm UTC](https://discuss.opensource.org/t/welcome-diverse-approaches-to-training-data-within-a-unified-open-source-ai-definition/531/4 "2024-08-30T21:39:56Z")

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Thanks for your comments @arandal and @mjbommar. Indeed working with organizations such as Creative Commons who maintain the ecosystem of “data licenses” is critical. Some of the organizations that we are constantly in touch with include [MLCommons](https://mlcommons.org/), the [Open Future Foundation](https://openfuture.eu/) and the [Data and Trust Alliance](https://dataandtrustalliance.org/work/data-provenance-standards). The latter one has recently published the [Data Provenance Standards](https://dataandtrustalliance.org/work/data-provenance-standards) which we highly recommend.

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