Navigating the responsible AI landscape: unraveling the principles-to-practices gap of transparency and explainability at the BBC

Transparency and explainability are often considered critical to the development and implementation of responsible AI. In an era increasingly dominated by algorithmic decision-making, this study wants to better understand how principles like transparency and explainability are specifically translated into practices at the BBC. For this study, 22 expert interviews were conducted in 2023 to investigate the mechanisms shaping the principles-to-practices gap vis-a-vis responsible AI. Findings describe a discernible gap between stated principles and actual practices, specifically revealing complexities in ownership structures. While principles of transparency and explainability are articulated at an organizational level in the form of the BBC AI principles , their interpretation and execution vary across different departments and specific stakeholders. This research maps the oftentimes precarious journey of implementing responsible AI, highlighting divergences between organizational intent and individual departmental applications. One emerging pattern highlights the lack of clearly defined roles and responsibilities of specific BBC departments when it comes to responsible AI in relation to transparency and explainability. The absence of these roles and responsibilities may lead to ambiguity and confusion, potentially resulting in an uneven distribution of responsible AI practices.