The BBC is the world’s largest public service broadcaster. It reaches every week more than 80% of the UK’s adult population and 279 million people worldwide. In order to ensure that our audiences get the most engaging experience, our team develops recommender systems which aim provide users with the most relevant pieces of content among the thousands the BBC publishes every day. All BBC output should serve the organisation’s mission to “act in the public interest, serving all audiences through the provision of impartial, high-quality and distinctive output and services which inform, educate, and entertain”. Recommendations make no exception and, since they determine what our audiences see, they are in effect editorial choices at scale. How can we ensure that our recommendations are consistent with our mission and public service values, avoiding some of the harmful effects which might be associated with recommenders? In addressing this question, we identified two main challenges: (i.) methodological challenges: public service values are hard to pin down into a specific metric, therefore we have no clearly defined optimisation function for our recommenders; (ii.) cultural/operational challenges: domain knowledge around public service values sits with our editorial staff, whereas data scientists are the recommendations specialists. We need to create a shared understanding of the problem and a common language to describe objectives and solutions across data science and editorial.
In my talk, I will describe the approach we devised to tackle these challenges, present a use case from our work on a BBC product, and reflect upon the lessons learnt.
Technical level: Introductory level/students (some technical knowledge needed)