Update onosaid.mdx

Author: tarunima

src/blog/onosaid.mdx

@@ -6,7 +6,7 @@ tags: open source, responsible AI
 author: Tarunima
 ---
 
-I'll start with some belated thoughts on the [OSAID definition](https://opensource.org/ai/open-source-ai-definition) that was released last week. At IndiaFOSS in September this year, I spoke about the OSAID and urged people to give feedback on it. I was concerned that people weren't following a very important development closely enough. A version of the talk was [published on the OSI blog](https://opensource.org/blog/data-transparency-in-open-source-ai-protecting-sensitive-datasets) that sparked some discussion on the [OSI forum](https://discuss.opensource.org/t/data-transparency-in-open-source-ai-protecting-sensitive-datasets/588 "Data Transparency in Open Source AI: Protecting Sensitive Datasets"), but this wasn't a conversation that I wanted to casually dip into. I am sharing here, some more considered reflections.
+I'll start with some belated thoughts on the [Open Source AI definition](https://opensource.org/ai/open-source-ai-definition) that was released last week. At IndiaFOSS in September this year, I spoke about the OSAID and urged people to give feedback on it. I was concerned that people weren't following a very important development closely enough. A version of the talk was [published on the OSI blog](https://opensource.org/blog/data-transparency-in-open-source-ai-protecting-sensitive-datasets) that sparked some discussion on the [OSI forum](https://discuss.opensource.org/t/data-transparency-in-open-source-ai-protecting-sensitive-datasets/588 "Data Transparency in Open Source AI: Protecting Sensitive Datasets"), but this wasn't a conversation that I wanted to casually dip into. I am sharing here, some more considered reflections.
 
 Some background- Tattle's work is open source and has always had a machine learning (AI) component to it. While the open source AI rhetoric reached a crescendo with the release of large language models, it has been background chatter as far back as I can remember. ML developers thinking about licenses have had to do the math of openness based on the components they've used. Questions we've asked ourselves in context of [Feluda](https://github.com/tattle-made/feluda)- is it open source if it relies on Resnet? BERT? What if we use Google Cloud Vision API as one layer of data processing?  These were also questions that we had to answer when submitting Feluda to the [DPGA registry](https://www.digitalpublicgoods.net/submission-guide "Submission Guide » Digital Public Goods Alliance"). And then again, [for Uli](https://github.com/tattle-made/Uli "GitHub - tattle-made/Uli: Software and Resources for Mitigating Online Gender Based Violence in India").