Evaluating the Recognisability of AI-Generated Familiar Images in a Closed Environment with a Gamified Approach

Abstract

This paper presents a case study on a gamified approach for evaluating AI-generated images, with a focus on recognition accuracy in a controlled, familiar environment. As generative AI becomes broadly accessible, concerns are growing about the creation of non-consensual or harmful content. Therefore, it is important to evaluate the capacity of AI to generate recognisable images of known people or animals, particularly in familiar and close settings. This case study aimed to investigate whether generative AI can accurately produce identifiable images based on brief textual descriptions and to explore the impact of cognitive biases and description quality on recognition. The study involved participants from the same workplace that were asked to provide textual descriptions of their colleagues and animals, which were used to generate images. These images were evaluated in a competitive quiz format to measure recognition accuracy. Findings reveal that familiarity significantly enhances recognition, though cognitive biases may influence participants' accuracy. Our gamified approach provides a streamlined and engaging framework for AI evaluation. However, the limited setting of the study and dataset suggest further research is needed to assess generalizability.

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