Rethinking QoE in the Age of AI: From Algorithms to Experience-Based Evaluation

AI evaluation is undergoing a paradigm shift from focusing solely on algorithmic accuracy of AI models to emphasizing experience-based assessment of human interactions with AI systems. Under frameworks like the EU AI Act, evaluation now considers intended purpose, risk, transparency, human oversight, and real-world robustness alongside accuracy. Quality of Experience (QoE) methodologies may offer a structured approach to evaluate how users perceive and experience AI systems in terms of transparency, trust, control and overall satisfaction. This column gives inspiration and shared insights for both communities to advance experience-based AI system evaluation together.