Empowering Users Together: Connecting Algorithmic Collective Action and Explainable AI

Algorithmic Collective Action (ACA) and Explainable AI (XAI) both aim to empower users, yet approach this goal differently: XAI tends to explain "why'' algorithms make a prediction, while ACA tends to focus on "how'' users can collectively influence outcomes. Despite their similar goals and challenges, there is a notable lack of research that addresses or connects both fields. Another related field, algorithmic recourse research, highlights this intersection but remains mostly limited to individual interventions. In this paper, we analyze conceptual overlaps between XAI and ACA, identify practical opportunities for integration, and provide examples showing how XAIs' explanatory methods can enhance ACA strategies while ACA work can inform more actionable XAI design. Our findings support that combining explanation and action can more effectively advance user understanding, agency, and algorithmic transparency.