To Explain Or Not To Explain: An Empirical Investigation Of AI-Based Recommendations On Social Media Platforms
This addresses comprehensibility and transparency issues in social media recommendations for end users, but it is incremental as it focuses on qualitative analysis without proposing new methods.
The paper investigated user perceptions of AI-based social media recommendations, finding that users primarily need explanations for unfamiliar content and data security, and that concise, non-technical explanations improve transparency, trust, and understandability.
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.