Trust Building in AI systems: A critical thinking perspective
How do I know when to trust AI,and when not to?
Who goes to jail if a self driving car kills someone tomorrow?
Do you know scientists say people will believe anything,repeated enough
Designing AI systems is also an exercise in critical thinking because an AI is only as good as its creator.This talk is for discussions like these,and more.
With the exponential increase in computing power available, several AI algorithms that were mere papers written decades ago have become implementable. For a data scientist, it is very tempting to use the most sophisticated algorithm available. But given that its applicability has moved beyond academia and out into the business world, are numbers alone sufficient? Putting context to AI, or XAI (explainable AI) takes the black box out of AI to enhance human-computer interaction. This talk shall revolve around the interpret-ability-complexity trade-off, challenges, drivers and caveats of the XAI paradigm, and an intuitive demo of translating inner workings of an ML algorithm into human understandable formats to achieve more business buy-ins.
Prepare to be amused and enthralled at the same time.
Outline/Structure of the Talk
- Introduction ( 2 mins)
- Need for XAI ( 2 mins)
- Introduction to the explainable AI paradigm ( 2mins)
- How cognitive distortions manifest in AI ( 2 mins)
- Attempts at building explain-ability in AI systems (Code level,audit level and industry attempts) ( 10 mins)
- Future scope of work ( 2 mins)
Learning Outcome
Any scientifically designed system is only as good or as bad as its creator.
At the end of this session,users walk away with an understanding of appreciation of how human thought process influences an AI design process.They shall also be able to critically evaluate an existing AI implementation and weigh on its pros and cons without getting into the inner workings of the actual algorithm.
Building upon my previous talks at ODSC Delhi's first ever meetup (June 2019) and Google Devfest New Delhi 2019,This part explores attempts at reconciling cognition and meta-cognition.
Finally,the next time someone tries to tell them 9 out of 10 dentists recommend brand X,they know what they're hearing is only a part of the bigger picture.
Target Audience
This is for people who have already started using AI,or are skeptical about it,Any existing practitioner can walk away with new insights,and newbies can expect to find a new avenue of thinking.
Prerequisites for Attendees
Working knowledge of R/Python/Basic ML is good to have,but an inquisitive mindset beats everything.
Links
A portion of the talk presented at ODSC Delhi's first meetup
TL;DR of Google Devfest New Delhi talk
https://commudle.com/gdg-new-delhi/events/devfest-19/session-discussions?speaker_resource=63
Also presented at Pydata (backed by NumFOCUS) Delhi.
Google scholar profile: https://scholar.google.com/citations?user=Xfwf_HgAAAAJ&hl=en
schedule Submitted 4 years ago
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References:
- "https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers":https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers
- "https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429":https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429
- "https://www.bloomberg.com/news/articles/2016-09-21/spotify-is-perfecting-the-art-of-the-playlist":https://www.bloomberg.com/news/articles/2016-09-21/spotify-is-perfecting-the-art-of-the-playlist
- "https://dl.acm.org/citation.cfm?id=1864770":https://dl.acm.org/citation.cfm?id=1864770
- "Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modelling": https://arxiv.org/pdf/1810.12027.pdf
- "Deep Reinforcement Learning for Page-wise Recommendations": https://arxiv.org/pdf/1805.02343.pdf
- "Deep Reinforcement Learning for List-wise Recommendations": https://arxiv.org/pdf/1801.00209.pdf
- "Deep Reinforcement Learning Based RecSys Using Distributed Q Table": http://www.ieomsociety.org/ieom2020/papers/274.pdf
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