Human vs. Model Agreement: How Inter-Rater Consistency Shapes Benchmark Reliability

When human annotators disagree, it raises a critical question: how can we trust an AI model trained on that data? This question highlights a major challenge in AI development. AI systems depend on human-labeled data to learn and improve. But when human annotators disagree, the data becomes unreliable, and so do the benchmarks we use to judge model performance.

Read this blog to explore how IRC affects the reliability of benchmarks and how it shapes the way we evaluate models.