The Explanatory Gap in Algorithmic News Curation

TL;DR

This paper investigates the effectiveness of explanations in helping expert users, journalists, understand ML-based news curation, revealing a significant gap between available explanations and user needs.

Contribution

It introduces the concept of the Explanatory Gap, highlighting the disconnect between explanation methods and user understanding in news recommendation systems.

Findings

None of the explanations were perceived as helpful by expert users

Identifies a gap between explanation availability and user comprehension

Highlights the need for more effective explanation methods

Abstract

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.