mathbabe

Today

Today I’m super excited to go to the opening launch party of danah boyd’s Data and Society. Data and Society has a bunch of cool initiatives but I’m particularly interested in their Council for Big Data, Ethics, and Society. They were the people that helped make the Podesta Report on Big Data as good as it was. There will be a mini-conference this afternoon I’m looking forward to very much. Brilliant folks doing great work and talking to each other across disciplinary lines, can’t get enough of that stuff.

This weekend

This coming Saturday I’ll be moderating a panel called Spotlight on Data-Driven Journalism: The job of a data journalist and the impact of computational reporting in the newsroom at the New York Press Club Conference on Journalism. The panelists are going to be great:

• John Keefe @jkeefe, Sr. editor, data news & J-technology, WNYC
• Maryanne Murray @lightnosugar, Global head of graphics, Reuters
• Zach Seward @zseward, Quartz
• Chris Walker @cpwalker07, Dir., data visualization, Mic News

The full program is available here.

December 12th

In mid-December I’m on a panel myself at the Fairness, Accountability, and Transparency in Machine Learning Conference in Montreal. This conference seems to directly take up the call of the Podesta Report I mentioned above, and seeks to provide further research into the dangers of “encoding discrimination in automated decisions”. Amazing! So glad this is happening and that I get to be part of it. Here are some questions that will be taken up at this one-day conference (more information here):

• How can we achieve high classification accuracy while eliminating discriminatory biases? What are meaningful formal fairness properties?
• How can we design expressive yet easily interpretable classifiers?
• Can we ensure that a classifier remains accurate even if the statistical signal it relies on is exposed to public scrutiny?
• Are there practical methods to test existing classifiers for compliance with a policy?