Data Ethnography for AI

Abstract: Data Enthography is a look at data as a means to combat bias and produce data to intervene in larger civic and private networks and engagements. This presentation uses insights from machine learning analytics and design thinking to challenge those developing algorithms and data sets that are to be representative of diverse populations but rarely are. The talk is largely based on the concept that to illuminate bias within machine learning, the ‘removal of bias’ itself has to be manifested into a ‘thing’ to teach or sway the algorithms. The idea aims to initiate a standard for equity and equality, by centering collaboration in the creation of this data set. The application has affects within areas of biometrics of accurate facial recognition, predictive analytics and finance and credit issuances.

Bio: Nicole Alexander is an Adjunct Professor at New York University. She is also a Lecturer at Columbia University and an Advisory Council Subject Matter Expert at Gerson Lehrman Group. Over the past 16 years Nicole has held leadership roles across marketing and technology which included Vice President, Innovation Practice at Nielsen China, Vice President at Marketing Evolution, and Vice President, at Pointlogic.

Nicole holds a global executive M.B.A. jointly awarded by New York University Stern School of Business, HEC Paris and London School of Economics and Political Science. She earned a Bachelor's degree in International Business from New York University.

Open Data Science Conference