Can we trust AI evaluation? Modern AI systems are judged through benchmarks, aggregate scores, and public leaderboards, yet trust in these evaluations is often assumed rather than demonstrated. An evaluation can be precise but measure the wrong construct, stable on a familiar benchmark but brittle on newly collected data, or impressive on a leaderboard while poorly aligned with real-world decisions. Repeated benchmark use, small perturbations, underreported variance, leakage, and contamination can further weaken the evidence behind evaluation claims. The TAE (Trust-AI-Eval): Can We Trust AI Evaluation? workshop treats evaluation itself as an object of study: what is measured, which assumptions connect a protocol to a claim, how uncertainty and failure modes are reported, and when the resulting evidence is strong enough to guide deployment. By bringing together work on robustness, causal and measurement validity, auditing, judge reliability, and deployment risk, the workshop aims to clarify when AI evaluation results deserve trust and how evaluation practices can become more reliable, transparent, and decision-relevant.