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Moving Fast With Broken Data: Implementing an Automatic Data Validation System for ML Pipelines

I recently came across an insightful research paper titled "Moving Fast With Broken Data" by Shreya Shankar, Labib Fawaz, Karl Gyllstrom, and Aditya G. Parameswaran from UC Berkeley and Meta. The paper addresses the significant issue of data corruption in machine learning (ML) pipelines, which often leads to decreased model accuracy. The authors present an…

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MIT Covid19 Datathon

Last week, I had the opportunity to participate as a mentor in the MIT COVID-19 Datathon working on coronavirus infodemic to identify myths, misinformation, and fake news associated with the pandemic. Out of 2,750 participants and 412 teams, our team qualified to be the top 10 semi-finalists. The MIT COVID-19 Datathon is a week-long virtual…

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Building Responsible AI - Taming the Bigot in the Machine for Trustable Models

Artificial Intelligence holds a great promise for mankind --- enterprises, education, government, public policy, building knowledge economy, and data driven decision making, we see the emergence of cognitive computing in different walks of life. However, the question of reliable, accountable, and fair artificial intelligence is still far from being answered. Michael McQuade, member of the…

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AI's Ethical Dilemma - Confessions of Angry Programmers Podcast

My interview on the podcast, confessions of an Angry Programmer is live on itunes. https://itunes.apple.com/us/podcast/confessions-of-angry-programmers-podcast/id1450294658?mt=2 Thank you David and Woody for inviting me to speak. My topic included explain-ability in AI algorithms which includes both technical and social perspectives. In this podcast we discussed algorithmic bias, black-box nature of algorithms, and need for transparency. I…

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