Data & Society

Sonia Livingstone on where and why do digital media – and digital media learning – fit into the lives of young teenagers living in complex urban societies? Do they help build valued connections, or enhance opportunities to create, learn and participate? Or do they lead to hyper-connection, surveillance and loss of privacy for young people? Reflecting on a year’s ethnography (free to read at http://connectedyouth.nyupress.org/) with a class of 13 year olds, exploring their sites of living and learning online and offline, Sonia argues that their understandable desire for ‘positive disconnections’ means crucial opportunities to learn are being missed. These might be overcome with a more child-centered or even child-rights approach to the digital age.

In this Databite, Neil Selwyn works through some emerging headline findings from a new three year study of digital technology use in Australian high schools. In particular Neil highlights the ways in which schools’ actual uses of technology often contradict presumptions of ‘connected learning’, ‘digital education’ and the like. Instead Neil considers…

• how and why recent innovations such as maker culture, personalised learning and data-driven education are subsumed within more restrictive institutional ‘logics’;

The talk provides plenty of scope to consider how technology use in schools might be ‘otherwise’, and alternate agendas to be pursued by educators, policymakers, technology developers and other stakeholders in the ed-tech space.

Elana Zeide on Student Privacy and Big Data. With the rise of online learning environments, student records are no longer just basic academic and administrative information, but include data and metadata generated from student interaction with digital platforms as well as unexpected sources like student ID badges and social media. Applying big data analytics to this wealth of information has the potential to revolutionize education, but also risks unintended consequences that affect the core values of the education system as well as civil rights and liberties.

The current student privacy regulatory regime does not address the issues raised by modern information technology and data-driven decision-making in education. This presentation highlights key issues of the student privacy debate, proposed reforms, and emerging legal and ethical issues, as well as implications of data-driven education environments and decision-making that extend far beyond school settings.

Madeleine Clare Elish presents “An AI Pattern Language,” coauthored with Tim Hwang. The publication is the culmination of two years of research and conversations with a range of industry practitioners working in intelligent systems and artificial intelligence. The work was supported by the John D. and Catherine T. MacArthur Foundation. You can purchase your own copy or download the PDF at autonomy.datasociety.net.

Kristian Lum will elaborate on the concept of “bias in, bias out” in machine learning with a simple, non-technical example. She will then demonstrate how applying machine learning to police records can result in the over-policing of historically over-policed communities. Using a case study from Oakland, CA, she will show one specific case of how predictive policing not only perpetuates the biases that were previously encoded in the police data, but – under some circumstances – actually amplifies those biases.