Leavey School Professor on the Social Implications of Big Data
The potential of applying big data in realms other than business was the topic of a recent article co-authored by a Santa Clara University Leavey School of Business professor.
Sanjiv Das is the Janice Terry Professor of Finance and Business Analytics at the Leavey School. His recent research has focused on machine learning, social networks, portfolio theory, and default risk modeling.
In the Zócalo Public Square article, Professor Das (and his co-authors, Bhagwan Chowdhry and Barney Hartman-Glaser of UCLA Anderson School of Management) discuss the notion of ‘hardwired bias’, in which individuals make decisions rooted in survival instincts. “It’s important, for example, to make a snap judgment about whether [a] … shape you see is a deadly poisonous snake or merely a rope. If you’re walking late one evening in a secluded, dimly lit neighborhood and you discern someone walking behind you, you need to figure out whether you might be attacked,” state the authors.
However, they continue, we often make egregious snap judgments based upon race, gender, age, or stature, resulting in profiling scenarios with dangerous, far-reaching consequences.
What if, the article suggests, we had access to a large amount of data in such situations, as opposed to a very limited set? “If we had access to a lot of data…and could process large amounts of [it] quickly and efficiently, we would use all data available to us in making predictions.”
The authors envision such technologies as phone scanners and wearable devices with motion sensors, while cautioning that major advances must be made before their responsible development and use.