It’s hard to be good. It takes effort, and nobody ever appreciates it anyway. It’s surprisingly easy to do evil when you have access to large volumes of data and an analytical mindset.
Duncan Ross and Francine Bennett offer a quick and easy guide to becoming an evil overlord of data without really trying. With (probably) anonymized examples from the real world, Duncan and Francine show how ordinary data scientists can make a real impact on the world around them with very little effort.
To do this you need to move beyond simply not doing good and enter the world of the evil data scientist. It’s a world where advanced inferences from the data you gave away without a thought make privacy a thing of the past, where corporations use the cover of “innovative disruption” to avoid laws, and where there is nothing more suspicious than a person who won’t reveal their data.
You could use this talk to consider how to avoid ethical dilemmas, develop ways to deal responsibly with data, or even do good. But that would be perverse.
No whales were hurt in creating this talk.
Duncan Ross is chief data officer at Times Higher Education. Duncan has been a data miner since the mid-1990s. Previously at Teradata, Duncan created analytical solutions across a number of industries, including warranty and root cause analysis in manufacturing and social network analysis in telecommunications. In his spare time, Duncan has been a city councilor, chair of a national charity, founder of an award-winning farmers market, and one of the founding directors of the Institute of Data Miners. More recently, he cofounded DataKind UK and regularly speaks on data science and social good.
Francine Bennett is a data scientist and the CEO and cofounder of Mastodon C, a group of Agile big data specialists who offer the open source Hadoop-powered technology and the technical and analytical skills to help organizations to realize the potential of their data. Before founding Mastodon C, Francine spent a number of years working on big data analysis for search engines, helping them to turn lots of data into even more money. She enjoys good coffee, running, sleeping as much as possible, and exploring large datasets.
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