Ensuring Ethical Practice in AI
Ensuring Ethical Practice in AI

Abstract: 

There have been multiple instances when a machine learning model was found to discriminate against a particular section of society, be it rejecting female candidates during hiring, systemically disapproving loans to working women, or having a high rejection rate for darker color candidates. Recently, it was found that facial recognition algorithms that are available as open-source have lower accuracy on female faces with darker skin color than vice versa. In another instance, research by CMU showed how a Google ad showed an ad for high-income jobs to men more often than women. Using credit risk data where we wanted to predict the probability of someone defaulting on a loan, we were able to shortlist features that were discriminatory in nature

Bio: 

Sray Agarwal is based in London and works for Publicis Sapient as a Manager of Data Science. His expertise lies in Predictive Modelling, Forecasting and advanced Machine Learning. He possesses a deep understanding of algorithms and advanced statistics. He has a background in management, economic and has done a master equivalent program in Data Science and Analytics. His current areas of interest are Fair and Explainable ML.

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

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