paper | 09.16.16
D&S researchers Alex Rosenblat and Tim Hwang analyze how widely captured data of technologies, which enable these technologies to make intelligent decisions, may negatively impact users.
More broadly, how might the power dynamics of user and platform interact with the marketing surrounding these technologies to produce outcomes which are perceived as deceptive or unfair? This provocation paper assembles a set of questions on the capacity for machine learning practices to create undisclosed violations of the expectations of users – expectations often created by the platform itself — when applied to public-facing network services. It draws on examples from consumer-facing services, namely GPS navigation services like Google Maps or Waze, and on the experiences of Uber drivers, in an employment context, to explore user assumptions about personalization in crowd-sourced, networked services.