Links about big bad data
August 26, 2015
There have been a lot of great articles recently on my beat, the dark side of big data. I wanted to share some of them with you today:
- An interview with Cynthia Dwork by Clair Cain Miller (h/t Marc Sobel). Describes how fairness is not automatic in algorithms, and the somewhat surprising fact that, in order to make sure an algorithm isn’t racist, for example, you must actually take race into consideration when testing it.
- How Google Could Rig the 2016 Election by Robert Epstein (h/t Ernie Davis). This describes the unreasonable power of search rank in terms political trust. Namely, when a given candidate was artificially lifted in terms of rank, people started to trust them more. Google’s meaningless response: “Providing relevant answers has been the cornerstone of Google’s approach to search from the very beginning. It would undermine the people’s trust in our results and company if we were to change course.”
- Big Data, Machine Learning, and the Social Sciences: Fairness, Accountability, and Transparency by Hannah Wallach (h/t Arnaud Sahuguet). She addresses the need for social scientists to work alongside computer scientists when working with human behavior data, as well as a prioritization on the question rather than data availability. She also promotes the idea of including a concept of uncertainty when possible.
- How Big Data Is Unfair by Moritz Hardt. This isn’t new but it is a fantastic overview of fairness issues in big data, specifically how data mining techniques deal with minority groups.
- How Social Bias Creeps Into Web Technology by Elizabeth Dwoskin (h/t Ernie Davis). Unfortunately behind the pay wall, this article talks about negative unintended consequences of data mining.
- A somewhat different topic but great article, The MOOC revolution that wasn’t, by Audrey Watters (h/t Ernie Davis). This article traces the fall of the mighty MOOC ideals. Best quote in the article: “High failure rates and dropouts are features, not bugs,” Caulfield suggests, “because they represent a way to thin pools of applicants for potential employers.”