The Explanatory Gap in Algorithmic News Curation
This addresses the challenge of making AI systems transparent for journalists and other experts in news curation, but it is incremental as it identifies a gap without proposing a solution.
The paper tackled the problem of explaining machine learning-based news curation systems to expert users, finding that none of the tested explanations were perceived as helpful, indicating a gap between available explanations and user needs.
Considering the large amount of available content, social media platforms increasingly employ machine learning (ML) systems to curate news. This paper examines how well different explanations help expert users understand why certain news stories are recommended to them. The expert users were journalists, who are trained to judge the relevance of news. Surprisingly, none of the explanations are perceived as helpful. Our investigation provides a first indication of a gap between what is available to explain ML-based curation systems and what users need to understand such systems. We call this the Explanatory Gap in Machine Learning-based Curation Systems.