Against Opacity: Explainable AI and Large Language Models for Effective Digital Advertising

TL;DR

The paper addresses opacity in digital advertising platforms and the challenge advertisers face in predicting ad performance and interpreting AI-driven decisions. It proposes a dual framework: SoWide-v2 for multimodal CTR prediction with attention-based visual explanations and SODA, an LLM-based advertising analysis framework that generates human-friendly explanations, personas, and content recommendations. The case study with industry professionals indicates high perceived value and potential for practical adoption, suggesting a pathway toward explainable, data-driven marketing. This work advances transparent AI-assisted advertising by coupling state-of-the-art CTR models with LLM-based narrative generation and explainability tools.

Abstract

The opaqueness of modern digital advertising, exemplified by platforms such as Meta Ads, raises concerns regarding their autonomous control over audience targeting, pricing structures, and ad relevancy assessments. Locked in their leading positions by network effects, ``Metas and Googles of the world'' attract countless advertisers who rely on intuition, with billions of dollars lost on ineffective social media ads. The platforms' algorithms use huge amounts of data unavailable to advertisers, and the algorithms themselves are opaque as well. This lack of transparency hinders the advertisers' ability to make informed decisions and necessitates efforts to promote transparency, standardize industry metrics, and strengthen regulatory frameworks. In this work, we propose novel ways to assist marketers in optimizing their advertising strategies via machine learning techniques designed to analyze and evaluate content, in particular, predict the click-through rates (CTR) of novel advertising content. Another important problem is that large volumes of data available in the competitive landscape, e.g., competitors' ads, impede the ability of marketers to derive meaningful insights. This leads to a pressing need for a novel approach that would allow us to summarize and comprehend complex data. Inspired by the success of ChatGPT in bridging the gap between large language models (LLMs) and a broader non-technical audience, we propose a novel system that facilitates marketers in data interpretation, called SODA, that merges LLMs with explainable AI, enabling better human-AI collaboration with an emphasis on the domain of digital marketing and advertising. By combining LLMs and explainability features, in particular modern text-image models, we aim to improve the synergy between human marketers and AI systems.

Figures (4)

• Figure 1:
• Figure 1:
• Figure 4:
• Figure 8: Sample user persona generation results.