2020 IASA - FIAT/IFTA Joint Conference

There is a growing evidence base around diversity and equal opportunities in television. In the UK, the broadcasting regulator Ofcom has published the annual Diversity in Broadcasting report since 2017, requiring the main five UK broadcasters to provide data on legally protected characteristics such as gender, racial group, and disability of workers, both on and off screen.

Nesta (an innovation foundation) and Learning on Screen (who operate BoB, an on demand TV and radio service for education) have partnered to investigate how to apply novel methods to archived broadcast content for more efficient diversity monitoring.

Using a sample of broadcast content from free-to-air channels, we apply computer vision and machine learning methods (including face detection and facial attribute classification algorithms using deep learning) to analyse the characters who are shown on screen and how they are represented on British television. We discuss the findings and challenges of using these novel research methods, including those posed by copyright law. Getting permission from rights holders to use large amounts of copyright protected works for AI purposes is often impractical, if not impossible. In this context, can users of audiovisual archives rely on copyright exceptions? How can archivists assist and advise users who wish to use protected materials under exceptions? This parallel session presentation will explore these challenging questions as well as other ethical, socio-legal and technical implications.