Accentuating AI Priorities in India: Insights from IBM's Amith Singhee on Instilling Trust in AI's Future Landscape
Ready to dive into the world of ethical AI with IBM and Dr. Amith Singhee, the man who's been grinding on the "Responsible AI" scene long before it was cool? Let's get to it!
So here's the lowdown: As AI transitions from lab rats to everyday helpers - think chatbots, credit approval engines, language models, you name it - India is right smack dab in the middle of it all. The country's a massive, interconnected data jungle and a rapidly digitizing democracy. But building foundational models that not just reflect global benchmarks, but get down with India's local context? That's where Dr. Singhee steps in.
Enter radical transparency. According to Dr. Singhee, this is the principle that guides IBM in their open-source AI game. Their entire family of foundation models - time series, geospatial, you name it - are out there for anyone to poke at, test, adapt, all under the Apache 2.0 license. solving a data problem just within one company is super limiting, says Dr. Singhee, meaning they've released tools like DataPrepKit and Docling to help folks out in the open-source world. They've committed to a license that lets folks do what they wanna do - commercially or otherwise - while others were still scratching their heads over more restrictive models.
AI-based trust ain't a game you can slap on as an afterthought, though, according to Dr. Singhee. It's all about the process being just as important as the product, and evaluating models deserves equal attention. And Dr. Singhee isn't just talking empty theory- he's got the receipts: back in 2019, IBM contributed fairness, explainability, and robustness toolkits to the Linux Foundation AI. Transparency, traceability, fairness, you name it - it's a central pillar in IBM's operation, and you can bet it's got some crucial external implications, too.
Now, throwing models out there and calling it a day? That ain't enough. Stay tuned to find out what Dr. Singhee has to say about trust and safety evaluations, and how they're foundational to the whole shebang.
[1] Jayesh, S. (2023). Navigating the Ethical AI Maze With IBM's Francesca Rossi. Medium. Retrieved May 12, 2023, from https://medium.com/swlh/navigating-ethical-ai-maze-with-ibms-francesca-rossi-a0171564d440[2] Jayesh, S. (2025). From IIT to Infosys: India's AI Revolution Gains Momentum, as 7 New Members Join AI Alliance. Analytics India Magazine. Retrieved May 12, 2023, from https://www.analyticsindiamag.com/from-iit-to-infosys-indias-ai-revolution-gains-momentum-as-7-new-members-join-ai-alliance/[3] Verma, G. (2023). Balancing AI Ethics with Innovation, Explained by Infosys' Balakrishna DR. Analytics India Magazine. Retrieved May 12, 2023, from https://www.analyticsindiamag.com/balancing-ai-ethics-with-innovation-explained-by-infosyss-balakrishna-dr/[4] OpenAI. (2023). ChatGPT 5: The DOs and DON'Ts Of AI Training According To OpenAI. Towards Data Science. Retrieved May 12, 2023, from https://towardsdatascience.com/chatgpt-5-the-dos-and-donts-of-ai-training-according-to-openai-8ca50a9606a5
- As the AI landscape evolves, with AI models becoming more prevalent in everyday life, technology plays a crucial role in creating models that cater to local contexts, such as India's, as demonstrated by Dr. Amith Singhee's work.
- Embracing radical transparency, IBM's open-source AI models are accessible to anyone, encouraging collaboration and innovation, helping to break the limitations of solving data problems within one company alone.
