Context-Aware Visualization for Explainable AI Recommendations in Social Media: A Vision for User-Aligned Explanations

TL;DR

This paper proposes a novel context-aware visualization system for explainable AI recommendations in social media, aiming to improve user understanding and trust by tailoring explanations to diverse user needs and contexts.

Contribution

It introduces a unified framework that adapts explanation style and granularity within a single pipeline, addressing the gap in user-specific explainability in social media AI recommendations.

Findings

Framework supports multiple explanation styles and granularities

Pilot study with 30 users to evaluate decision-making impact

Enhances user trust and understanding of AI recommendations

Abstract

Social media platforms today strive to improve user experience through AI recommendations, yet the value of such recommendations vanishes as users do not understand the reasons behind them. This issue arises because explainability in social media is general and lacks alignment with user-specific needs. In this vision paper, we outline a user-segmented and context-aware explanation layer by proposing a visual explanation system with diverse explanation methods. The proposed system is framed by the variety of user needs and contexts, showing explanations in different visualized forms, including a technically detailed version for AI experts and a simplified one for lay users. Our framework is the first to jointly adapt explanation style (visual vs. numeric) and granularity (expert vs. lay) inside a single pipeline. A public pilot with 30 X users will validate its impact on decision-making and trust.