March 30, 2017

The workshop on Data and Algorithmic Transparency

From online advertising to Uber to predictive policing, algorithmic systems powered by personal data affect more and more of our lives. As our society begins to grapple with the consequences of this shift, empirical investigation of these systems has proved vital to understand the potential for discrimination, privacy breaches, and vulnerability to manipulation.

This emerging field of research, which we’re calling Data and Algorithmic Transparency, seems poised to grow dramatically. But it faces a number of methodological challenges which can only be solved by bringing together expertise from a variety of disciplines. That is why Alan Mislove and I are organizing the first workshop on Data and Algorithmic Transparency at Columbia University on Nov 19, 2016.

Here are three reasons you should participate in this workshop.

  1. Start of a new, interdisciplinary community. The set of disciplines represented on the Program Committee is strikingly diverse: Internet measurement, information privacy/security, computer systems, human-computer interaction, law, and media studies. Industrial research and government are also represented. We expect the workshop itself to have a similar mix of participants, and that is exactly what is needed to make transparency research a success. Alan and I (and others including Nikolaos Laoutaris) are committed to growing and nurturing this community over the next several years.
  1. Co-located with two other exciting events: the Data Transparency Lab conference (DTL ‘16) and the Fairness, Accountability, and Transparency in Machine Learning workshop (FAT-ML ‘16). DTL shares many of the goals of the DAT workshop, but is non-academic. FAT-ML has a complementary relationship with the goals of DAT: it seeks to develop machine learning techniques for developers of algorithmic systems to improve fairness and accountability, whereas DAT seeks to analyze existing systems, typically “from the outside”. The events are consecutive and non-overlapping, and participants of each event are encouraged to attend the others.
  1. A format that makes the most of everyone’s time. At most computer science conferences, each speaker mumbles through their slides while the audience is a sea of laptops, awaiting their turn. DAT will be the opposite. We plan to have paper discussions instead of paper presentations, with commenters and participants, rather than authors, doing most of the speaking about each paper. This first edition of DAT will be non-archival (but peer-reviewed), and one goal of the discussions is to help authors improve their papers for later publication. We are also soliciting talk proposals about already published work; groups of accepted talks will be organized into panels.

See you in New York City!