Visual Explanation for Advertising Creative Workflow

Shoko Sawada, Tomoyuki Suzuki, Kota Yamaguchi, Masashi Toyoda

Abstract: Explainable AI (XAI) attempts to produce interpretable results from highly complex AI systems, but its form and effectiveness vary depending on the application domain. In this paper, we explore how XAI techniques can help graphic designers work on advertising materials. A creative domain such as graphic design is often characterized by a weak connection between the individual work and the business goal; e.g., a small change in the design of a banner can result in a huge difference in the audience’s reaction. We develop an XAI system for designers that provides visual feedback explaining which component of the design is likely to affect the business metric. Our user study shows that with our system, designers complete the project in fewer iterations and in less time to achieve the desired quality of work compared to naive score-based feedback. These findings highlight the benefits of leveraging XAI in creative domains.