Public Service Algorithms

As Internet users, we are increasingly used to encountering database algorithms in the form of recommendation engines - from shopping services that provide further purchase options, 'if you liked this, you might also like …', through to personalised newsfeeds on social media platforms and beyond.
Television has been quick to adopt such tactics, particularly through new players such as Netflix and Amazon, becoming one of the key sites we encounter such algorithms. Such services promise us more of what we might like, tailoring content to our viewing preferences and habits. In the face of such algorithmic television tactics from online providers, traditional broadcasters have been forced to respond, including the public service broadcasters like the BBC and Channel 4. Here we find the BBC iPlayer and Channel 4's All4 service mimicking these algorithmic tactics, linking us to other episodes of the programme we've just watched and shows 'you may also like'. As these services move to 'sign in' systems, we might expect this trend to continue. But is that really what a public service broadcaster should do: recommend more of the same? Drawing on work from television studies and digital culture, as well as over a decade studying the multiplatform production practices and strategies of UK television, this paper argues for a notion of public service algorithms that would draw on television's broadcast past to secure the place of public service broadcasting in its digital future.