ABSTRACT. As algorithms pervade societal life, they are moving from the preserve of computer science to becoming the object of far wider academic and media attention. Many are now asking how the behaviour of algorithms can be made “accountable”. But why are they “opaque” and to whom? As this vital discussion unfolds in relation to Big Data in general, the Learning Analytics community must articulate what would count as meaningful questions and satisfactory answers in educational contexts. In this talk, I propose different lenses that we can bring to bear on a given learning analytics tool, to ask what it would mean for it to be accountable, and to whom. It turns out that algorithmic accountability may be the wrong focus.

BIO. Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, which he joined in August 2014 to direct the new Connected Intelligence Centre. Prior to that he was at The Open University’s Knowledge Media Institute 1995-2014. He brings a Human-Centred Informatics (HCI) approach to his work, with a background in Psychology (BSc, York), Ergonomics (MSc, London) and HCI (PhD, York). He co-edited Visualizing Argumentation (2003) followed by Knowledge Cartography (2008, 2nd Edn. 2014), and Constructing Knowledge Art (2015). He is active in the emerging field of Learning Analytics and is a co-founder of the Society for Learning Analytics Research, Compendium Institute and Learning Emergence network.

If you missed this CALRG session, you can find the recorded version of  Simon’s talk here