Measuring Creativity in the Age of Generative AI: Distinguishing Human and AI-Generated Creative Performance in Hiring and Talent Systems
This addresses the challenge for organizations in evaluating human creativity amidst AI-generated content, representing a novel method for a known bottleneck rather than incremental.
The paper tackled the problem of distinguishing human creativity from AI-generated content in hiring and talent systems by reconceptualizing creativity as a distributional and process-based property, and introduced a quantitative framework that aligns with intuitive judgments and reveals a structural shift toward bimodal distributions in AI-mediated environments.
Generative AI is rapidly transforming how organizations create value and evaluate talent. While large language models enhance baseline output quality, they simultaneously introduce ambiguity in assessing human creativity, as observable artifacts may be partially or fully AI-generated. This paper reconceptualizes creativity as a distributional and process-based property that emerges under shared constraints and competitive incentives. We introduce a quantitative framework for measuring creativity as novelty in synthesis, operationalized through idea generation and idea transformation within embedding space. Empirical evaluation demonstrates that the proposed metrics align with intuitive judgments of creativity while capturing distinctions that surface-level quality assessments miss. We further identify a structural shift toward bimodal distributions of creative output in AI-mediated environments, with implications for hiring, leadership, and competitive strategy. The findings suggest that in the age of generative AI, distinctiveness rather than fluency becomes the primary signal of human creative capability.