Black Box Learning Analytics? Beyond Algorithmic Transparency

In 2016 I started giving briefings on the meaning(s) of algorithmic accountability in education. This evolved into a tutorial that I ran at the 2017 Learning Analytics Summer Institute, a version of which was recorded at U. Michigan MOOC studios, but for various reasons, never edited together. I’m pleased to say that (thanks to our intern Ran Ding!) this is now available as a Creative Commons licensed resource. Reuse, chunk and remix please! [Compressed PDF slides 3.7Mb] [Powerpoint slides 27.9Mb]

Since 2016, activity around the ethics of Big Data/AI has exploded in an encouraging way, with many accessible resources becoming available (e.g. Data & Society Institute; AI Now Institute), as well as the emergence of the FATE (Fairness, Accountability, Transparency, Ethics) conference and network. However, there remain few resources specifically on the nature of, and responses to, algorithmic transparency and accountability in education, so this talk still seems relevant, and I welcome your feedback on this fast moving challenge.

The next steps would be to develop learning activities around this material to assist deeper engagement, and again, I’d love to hear from you if you want to move this forward.

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