A growing proportion of human activities such as social interactions, entertainment, shopping, and gathering information are now mediated by digital devices and services. Such digitally mediated activities can be easily recorded, offering an unprecedented opportunity to study and measure intimate psycho-demographic traits using actual--rather than self-reported--behavior. Our research shows that digital records of behavior, such as samples of text, Tweets, Facebook Likes, web-browsing logs, or even facial images can be used to accurately measure a wide range of traits including personality, intelligence, and political views. Such Big Data assessment has a number of advantages: it does not require participants' active involvement; it can be easily and inexpensively applied to large populations; and it is relatively immune to cheating or misrepresentation. If used ethically, it could revolutionize psychological assessment, marketing, recruitment, insurance, and many other industries. In the wrong hands, however, such methods pose significant privacy risks. In this talk, we will discuss how to reap the benefits of Big Data assessment while avoiding the pitfalls.
Michal Kosinski is the Assistant Professor in Organizational Behavior at Stanford University Graduate School of Business. His research focuses on humans in a digital environment and employs cutting-edge computational methods and Big Data mining. Michal received his PhD in Psychology from the University of Cambridge (UK) in 2014. Prior to his current appointment, he was as a Postdoctoral Scholar at Stanford University (Computer Science Department) and a researcher at Microsoft Research.