Anonymity in the context of Data-neighborhoods

The project investigates the transformation of anonymity in times of networked-data by looking into the history of urban
neighborhood design and its relation to neighborhood-related machine learning algorithms, such as k-nearest-neighbor (KNN).
This method analyzes behavioral patterns to form groups (neighborhoods) of actors with the same characteristics (neighbors).
This groupingis then deployed to understand and predict the actions of similar actors. KNN is used extensively at the exploratory
stages of model development and has been central to the production of algorithms designed to ‘personalize’ digital services
because it operates with cluster specific, therefore ‘anonymized’ instead of personal data (so it is, for example, not subject to
certain privacy restrictions). The work draws from extensive ethnographic with actors who have developed social media
neighborhood platforms and recommendation systems in Germany und Silicon Valley, to reveal how the relation of anonymity and
(data-)neighborhoods affects subjectivity, fairness and relations of equality and differenece in machine learning algorithms. At
SFU my work will be connected with undergraduate and graduate-level students and postdoctural researchers within the Digital
Democracies group to foster and integrate work on Vancouver neighborhoods and urban history.

Faculty Supervisor:

Wendy Hui Kyong Chun

Student:

Partner:

Leuphana Universität Lüneburg

Discipline:

Computer science

Sector:

Technology; New and Digital Media; Information and Communications Technology

University:

Simon Fraser University

Program: