Responsible & Safe AI Systems at ACM India ROCS at IIT Bombay
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Responsible & Safe AI Systems at ACM India ROCS at IIT Bombay
- 1. Responsible & Safe AI Feb 24, 2024 ROCS, IIT Bombay Ponnurangam Kumaraguru (“PK”) #ProfGiri CS IIIT Hyderabad ACM Distinguished Member TEDx Speaker https://precog.iiit.ac.in/ /in/ponguru @ponguru
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- 3. Know the Audience Students / Industry / Faculty / Others 3
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- 21. Changes in Predictions for Theme: Hatya (Murder) 22 Results
- 22. Changes in Predictions for Theme: Dahej (Dowry) 23 Results
- 23. Indian Legal Data Fair? 23 https://arxiv.org/pdf/2303.07247.pdf
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- 30. Any use cases / experiences from your side? 30
- 31. Similar systems / applications Bard by Google - is connected to internet, docs, drive, gmail LLaMa by Meta - open source LLM BingChat by Microsoft - integrates GPT with internet Copilot X by Github - integrates with VSCode to help you write code HuggingChat - open source chatGPT alternative BLOOM by BigScience - multilingual LLM OverflowAI by StackOverflow - LLM trained by stackoverflow Poe by Quora - has chatbot personalities YouChat - LLM powered by search engine You.com 31
- 32. Inconsistency in LLMs LLMs are not consistent, they often give contradictory answers to paraphrased questions. This makes them highly unreliable, untrustworthy, especially when the users are seeking advice.
- 33. Our Methodology
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- 36. Bias in LLMs Current systems like ChatGPT employ guardrails, and do not respond to biased content Users on the Web leave out key contexts, which make LLMs think the content is biased This negatively affects user engagement LLMs must be able to explore and ask more questions Our work aims to make LLMs bias-aware – context resolves confusion!
- 37. Context-Oriented Bias Indicator and Assessment Score
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- 41. Removing Harmful Knowledge Large pretrained models trained on low quality Internet corpora often have wrong / harmful data. Can we remove it post-hoc? Existing (GDPR, CCPA) and imminent legislation requires this Corrective Machine Unlearning to remove influences of label corruptions like noise, biases etc. in image classifiers Now expanding to removing harmful knowledge from LLMs Applications: Debiasing, denoising, removing harmful knowledge like weapon / toxic chemical design 41 https://arxiv.org/pdf/2402.14015.pdf
- 42. Takeaways? Growing fast More power comes with more responsibility More of us to study, critique these questions / topics Teaching this as a course on campus, content public 42
- 43. 43 Search for: Ponnurangam Kumaraguru https://www.linkedin.com/in/ponguru/ https://twitter.com/ponguru
- 45. Interested in working with us? 45 Full time Research Associates PhD Students Summer 2024 interns
- 48. Group pic & Selfie 48
Editor's Notes
- The counts for prediction changes in the Hindu names column are higher than the counts of changes in the Muslim names column. This shows that for the cases where model predictions are 0, the model switches its predictions to 1 more often when the proper noun is replaced by a Hindu name instead of being replaced by a Muslim name, indicating FOR THE THEME HATYA - MODEL HAS LEARNT that Hindus are more likely to get a bail granted.
- FOR THE THEME DAHEJ - MODEL HAS LEARNT that Hindus are NOT more likely to get a bail granted.
- I am done.. Happy to take any questions…