Recently, the White House opened a number of opportunities for the public to comment on the growing field of accountability for artificial intelligence (AI) systems. The National Telecommunications and Information Administration (NTIA), the Executive Branch agency that is principally responsible for advising the President on telecommunications and information policy issues, launched a comment process that […]
Refining the Concept of a Nutritional Label for Data and Models
May 3, 2018 by
By Julia Stoyanovich (Assistant Professor of Computer Science at Drexel University) and Bill Howe (Associate Professor in the Information School at the University of Washington) In August 2016, Julia Stoyanovich and Ellen P. Goodman spoke in this forum about the importance of bringing interpretability to the algorithmic transparency debate. They focused on algorithmic rankers, discussed the harms […]
Sign up now for the first workshop on Data and Algorithmic Transparency
October 28, 2016 by
I’m excited to announce that registration for the first workshop on Data and Algorithmic Transparency is now open. The workshop will take place at NYU on Nov 19. It convenes an emerging interdisciplinary community that seeks transparency and oversight of data-driven algorithmic systems through empirical research. Despite the short notice of the workshop’s announcement (about […]
The workshop on Data and Algorithmic Transparency
August 10, 2016 by
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 […]
