Data Science for Government 2016 Workshop
I attended the Data Science for Government workshop organised by the Behavioural Insights Team and the Blavatnik School of Government at Oxford last Wednesday. The programme is here. There were several workshop sessions and a wide-ranging panel discussion on the policy implications of developments in the use of data in public policy. It was good to hear more about the work of the Data Science Lab at Warwick and the work they are doing will likely be of interest to readers here. I was also glad to hear about the development of the Turing Institute. The ethical and behavioural issues surrounding the type of work they are conducting are very interesting and complex, and it was interesting to learn more about the political science input into the development of their work. Other talks I attended included Michael Luca's discussion of the use of big data to improve the operation of cities. This is well worth looking up (some of his papers below). Kate Glazebrook of BIT presented on their new hiring tool to reduce bias in recruitment (details here). David Spiegelhalter presented on communication of risk. His website on the communication of uncertainty gives a sense of his approach. There were several other very useful talks on the day and these links are a snapshot. It would be good to speak more in our networks about the implications of the proliferation of different types of data and analytic tools for policy and, in particular, the behavioural and ethical dimensions of these developments.
Glaeser, Edward L., Scott Duke Kominers, Michael Luca, and Nikhil Naik. "Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life." Economic Inquiry (forthcoming). View Details
Luca, Michael, Jon Kleinberg, and Sendhil Mullainathan. "Algorithms Need Managers, Too." Harvard Business Review 94, nos. 1/2 (January–February 2016): 96–101. View Details
Luca, Michael, Jon Kleinberg, and Sendhil Mullainathan. "Algorithms Need Managers, Too." Harvard Business Review 94, nos. 1/2 (January–February 2016): 96–101. View Details