We focus on using patent data, with machine learning methods, in the context of China, for the purpose of tracking the pace of development of potentially human rights sensitive smart city technologies.
News and Research articles on Machine learning
This paper introduces a socio-technical typology of bias in data-driven machine learning and artificial intelligence systems. It argues that a clear distinction must be made between different concepts of bias in such systems in order to analytically assess and politically critique these systems. By analysing the controversial Austrian “AMS algorithm” as a case study among other examples, this paper defines the following three types of bias: purely technical, socio-technical, and societal.
Smart technologies are capable of responding to feedback in ways that range from clever to mind-boggling. The point is how they affect human agency.