Kriti Doneria
Data Science Professional
Gurugram, India
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Kriti is a Kaggle Grandmaster, with 7 years of analytics experience across Fortune 500 giants specialising in BFSI, CPG and Healthcare.
TECH TALKS
TECH TALKS and FEATURES
** Featured in 'Women in AI Ethics'[https://womeninaiethics.org/directory/]
** Featured on the Podcast 'Would you Data scientist' on Responsible AI: https://omny.fm/shows/would-you-data-scientist/kriti-doneria-responsible-ai-and-data-and-decision
** Talk on Cognitive Biases and Ethics in AI at #IWD2021 #WTMUK&I here: https://youtu.be/MMSBVoeNfdw?t=9234
** Watch Kriti's talk on Cognitive Biases and Ethics in AI at #IWD2021 #WTMCasablanca #GDGMorocco here:
https://youtu.be/iUGiwmE2hwo?t=4677
** Talk on Introduction to Data Sonification at #TechKnowday #Womenshistorymonth celebration 2021 here: https://www.youtube.com/watch?v=dZHJhcOEl4U
** Talk on Explainable AI at ODSC Delhi meetup 2019 here: https://learnai.odsc.com/courses/explainable-ai-and-interpret-ability-of-ai-solutions-strategic-overview-challenges-and-caveats
** Talk in AI/ML track at Google Devfest New Delhi,2019, on Cognitive biases in AI: https://commudle.com/gdg-new-delhi/events/devfest-19/session-discussions?speaker_resource=63
Area of Expertise
Topics
Introduction to Data sonification
Abstract: The first step of any data science project is exploratory data analysis. While textual analysis and visual representations have been the norm, there has been a lot of research on other forms of translating data so as to make it easier for humans to comprehend the patterns inherent, or lack thereof.
This talk will take a look at data sonication ie translating data into sounds, it's applications, methods, drivers for adoption as well as a few interesting and recent use cases and a self-researched demo.
Cognitive biases in AI: Detection and mitigation
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.
Biases and Ethics in AI, An introduction
Biases are all around us, so what makes you think AI is different?
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