Finding Solutions For Algorithmic Fairness




Finding Solutions for Algorithmic Fairness

1 November 2018

New York

Added 01-Jan-1970

Co-hosting with Artificial Intelligence Hub to present an algorithmic fairness event. In our modern world of artificial intelligence/machine learning many companies, government agencies and hospitals are relying on algorithms and data to predict credit worthiness, preferred treatment for illnesses, job interviews, parole, and much more. As the evolution of machine learning continues to advance, having a better understanding of how to develop algorithms that are fair will become extremely important. Many current proprietary algorithms could have biases in the data or models that can potentially impact or have severe consequences in society.

Fortunately, many brilliant researchers and data scientists have started to look for solutions to address this serious challenge. Join us for a serious discussion with expert Data Scientists that are working on finding solutions and to implement more oversight when developing specific algorithms and machine learning products.


Eric Schles
Eric Schles works for Microsoft as a Data Scientist and also works as an Adjunct Professor and researcher at NYU. He specializes in using big data to combat human trafficking. Eric has worked for the Manhattan DA's Human Trafficking Response Unit, serving as Senior Analyst and for the White House under the Obama Administration in the past. He is deeply passionate about solving slavery and using data science and automation to do it. His research at NYU is focused on combating human trafficking.

Claudia Perlich
Claudia Perlich is a Senior Data Scientist at Two Sigma in New York City. Prior to her role at Two Sigma, she was the Chief Scientist at Dstillery where she designed, developed, analyzed, and optimized machine learning that drives digital advertising to prospective customers of brands. She started her career in Data Science at the IBM T.J. Watson Research Center, concentrating on research in data analytics and machine learning for complex real-world domains and applications. She tends to be domain agnostic having worked on almost anything from Twitter, DNA, server logs, CRM data, web usage, breast cancer, movie ratings and many more. Perlich is a very active public speaker and has published over 50 scientific publications as well as a few patents in the area of machine learning. She received her PhD in Information Systems from the NYU Stern School of Business and holds a Master of Computer Science from Colorado University.