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

One-size-fits-all explanations don't work: The paper identifies that current AI recommendation explanations on social media are "general and lack alignment with user-specific needs," meaning the same explanation shown to all users fails to help most of them understand why they're seeing particular content.

Different users need different types of explanations: The proposed system offers multiple explanation formats—including "a technically detailed version for AI experts and a simplified one for lay users"—recognizing that an engineer and a casual social media user require fundamentally different information to understand recommendations.

Visual explanations matter as much as technical ones: The framework jointly adapts both "explanation style (visual vs. numeric) and granularity (expert vs. lay)," suggesting that how explanations are presented (through charts, diagrams, or plain language) is as important as the content itself for helping users understand AI decisions.

Context shapes what explanations should say: The system is "context-aware," meaning explanations should adapt to individual circumstances and user backgrounds rather than providing identical explanations to everyone, potentially improving both comprehension and trust.

Real-world testing is underway: A pilot study with 30 X (formerly Twitter) users will test whether these tailored visual explanations actually improve "decision-making and trust," moving beyond theory to measure whether better explanations genuinely help users.