Masterclass Series 4: Helen Nissenbaum
Monday, 1 June 2015 from 15:00 to 17:00 (BST)
San Francisco, California
London, United Kingdom
Elements of Contextual Integrity as Guideposts for Privacy Research
According to the theory of contextual integrity, disruptions in the flow of personal information (often stemming from deployment of computational systems and digital media) are experienced as privacy threats not merely when they expose personal information or threaten our control over it but when they result in inappropriate flows. Appropriateness of flow is modeled by the construct of context-specific informational norms, which prescribe informational flows according to the parties involved (subjects, senders, recipients), the types of information, and constraints on flow between parties. Empirical studies promise a more nuanced, less ambiguous account of attitudes and behaviors relevant to privacy when taking account of these additional dimensions of analysis.
Helen Nissenbaum, Professor, Media, Culture, and Communication, NYU.
When & Where
Tobias Blanke, Mark Coté, Giles Greenway, and Jennifer Pyubs
The masterclass speaker series is born out of the research grant: “Our Data Ourselves”, undertaken at King’s College London. The focus of this project has been to investigate the personal data that gets generated by young people on their smartphone devices. Our aim has been to increase our knowledge and understanding of the nature and role of the data that young people produce when thy use platforms and applications on their mobiles.
After working with young people from Young Rewired State for the past 18 months, we would now like the opportunity to discuss our results within a larger dialogue that brings together scholars and hackers who are interested in big social data research. Over the coming months we are inviting you to take part in a wide range of topics that will examine mobile infrastructures and ecosystems, to issues related to the increasing amount of material that is ‘born digital’, to questions around privacy and the ethics of extracting user-generated data.