To Explain Or Not To Explain: An Empirical Investigation Of AI-Based Recommendations On Social Media Platforms

TL;DR

This study investigates user perceptions of AI-based social media recommendations, emphasizing the importance of explanations for transparency, trust, and user control, based on qualitative analysis of Facebook users.

Contribution

It provides empirical insights into user needs for explanations in social media recommendations and proposes a framework for improving transparency and user trust.

Findings

Users need explanations mainly for unfamiliar content and data security.

Concise, non-technical explanations improve user understanding.

Explanations positively influence perceived transparency and trust.

Abstract

AI based social media recommendations have great potential to improve the user experience. However, often these recommendations do not match the user interest and create an unpleasant experience for the users. Moreover, the recommendation system being a black box creates comprehensibility and transparency issues. This paper investigates social media recommendations from an end user perspective. For the investigation, we used the popular social media platform Facebook and recruited regular users to conduct a qualitative analysis. We asked participants about the social media content suggestions, their comprehensibility, and explainability. Our analysis shows users mostly require explanation whenever they encounter unfamiliar content and to ensure their online data security. Furthermore, the users require concise, non-technical explanations along with the facility of controlled information flow. In addition, we observed that explanations impact the users perception of transparency, trust, and understandability. Finally, we have outlined some design implications and presented a synthesized framework based on our data analysis.