arXiv:2606.30561v1 Announce Type: new Abstract: Modern AI evaluation frameworks treat evaluator disagreement as noise to be resolved. In creative domains, professional disagreement reflects genuine differences in taste, not measurement error. We argue that evaluating creative AI requires preserving two distinct signals: convergence, where professionals align around shared best practices, and divergence, where individual taste legitimately varies. We present the Human Creativity Benchmark (HCB), a benchmark that operationalizes this separation by collecting pairwise preferences, scalar ratings
The proliferation of generative AI in creative domains necessitates more sophisticated evaluation frameworks beyond simple objective metrics.
Accurately evaluating creative AI is crucial for its development, adoption, and integration into industries valuing subjective taste and professional judgment.
Current AI evaluation models, which treat evaluator disagreement as noise, will be superseded by benchmarks that differentiate between convergence and divergence in creative assessment.
- · AI art platforms
- · Creative industries using AI
- · AI ethics researchers
- · AI evaluation methods relying solely on objective metrics
- · Developers targeting only average preference
The Human Creativity Benchmark (HCB) offers a new standard for assessing AI's creative output, distinguishing between shared best practices and individual taste.
This differentiation could lead to the development of AI models specifically designed to cater to diverse aesthetic preferences, rather than a generalized 'good' output.
The nuanced understanding of 'creativity' enabled by such benchmarks might influence how human creativity itself is analyzed and valued in an AI-augmented world.
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Read at arXiv cs.AI