We’re increasingly dependent on algorithms in a variety of areas.  This has led to problems with algorithmic bias, in which statistical and econometric models or a programmed set of instructions systematically treats members of some groups differently than others. This can be due to the unconscious biases of engineers who build the models, biases in the data they are trained on, biases inherent in the models themselves or algorithms’ treatment of human attributes as single, isolated components rather than intersectional identities. We look at the ways in which models, programs, and algorithms in the media, marketing, and advertising industries can unintentionally favor majority populations and ignore or even discriminate against minority segments and how to overcome this problem. 

A conversation with,  Janelle James, SVP,  Ipsos IUU (Council Co-Chair), Kalinda Ukanwa,  Assistant Professor of Marketing at USC’s Marshall School of Business. Amanda Bower, Machine Learning Researcher at Twitter.  Hui Wang, VP/Director of Global Data Intelligence - Analytics Service, at Publicis Media.  Kathy Sheehan, Senior Vice President at Cassandra. Ilinca Barsan, Data Science Director at Wunderman Thompson.