Explanation has long been deemed a crucial aspect of accountability. By requiring that powerful actors explain the bases of their decisions — the logic goes — we reduce the risks of error, abuse, and arbitrariness, thus producing more socially desirable decisions. Decisionmaking processes employing machine learning algorithms and similar data-driven approaches complicate this equation. Such approaches promise to refine and improve the accuracy and efficiency of decisionmaking processes, but the logic and rationale behind each decision remains opaque to human understanding. The conference will grapple with the question of when and to what extent decisionmakers should be legally or ethically obligated to provide humanly meaningful explanations of individual decisions to those who are affected or to society at large.
Obfuscation strategies offer creative ways to evade surveillance, protect privacy, and improve security by adding, rather than concealing, data to make it more ambiguous and difficult to exploit. This interdisciplinary workshop convened researchers, scientists, developers, and artists to discuss a broad range of technical, theoretical, and policy approaches to obfuscation, from tools that anonymize users’ social media data to new methods for writing code itself. We surveyed some of the existing and emerging applications and technologies, threat models and scenarios for which obfuscation offers solutions, tests and tools for studying the strengths and weaknesses of obfuscation approaches, new challenges and applications (such as authentication, intellectual property, and security), benchmarks and approaches to formalizing obfuscation strategies, and general best practices for design, implementation, and evaluation of obfuscating systems.
AI Now: The Social and Economic Implications of Artifical Intelligence Technologies in the Near Term -- July 7, 2016
The White House and New York University’s Information Law Institute, with support from Google Open Research, Microsoft Research and the MacArthur Foundation hosted a major public symposium to address the near-term impacts of artifical intelligence technologies across social and economic systems. The focus was the challenges of the next 5-10 years, specifically addressing four themes: social inequality, labor, healthcare, and ethics. Leaders from industry, academia, and civil society will share ideas for technical design, research and policy directions.
At the Intersection of Privacy and Property -- May 6, 2016
This workshop provided a forum for work that explores the benefits and drawbacks of thinking about privacy—legally, practically, and conceptually—in terms of property. Given our common law heritage, the association between privacy and property is reflexive and ingrained. Nevertheless, significant problems arise in trying to make property norms “do the work” for privacy, particularly with respect to issues of data privacy. At the same time, it would also be wrong to dismiss property norms as wholly irrelevant to privacy. The right question, ultimately, is how property concepts and doctrines can be profitably incorporated into discussions of privacy—and likewise, to what extent we should wary about leaning too much on property-privacy analogy, in lieu of theorizing privacy independently.
Conference on Responsible Use of Open Data: Government and the Private Sector -- November 19-20, 2015
The Conference, co-organized by NYU Department of Media, Culture and Communication, and BCLT, addressed two related issues. The first was a set of normative challenges associated with the open data movement, including e.g. privacy and other civil liberties, equitable access to data, and what counts a public interest. The second addressed obligations of private/commercial holders of data to make their holdings available for public and research purposes. Panels included leading thinkers and actors representing a range of perspectives and positions. The conference kicked off with a keynote address by Dr. Amen Ra Mashariki, City of New York's Chief Analytics Officer in charge of the Mayor's Office of Data Analytics.