A new ARF Psych of GenAI experiment reveals that large language models apply a rigid, rule-driven logic when evaluating privacy scenarios—even when humans typically shift their reasoning based on framing, emotion and social context. Unlike consumers, who blend intuition, feeling and social perspective into their judgments, GPT-4o relied on a single internal rule across all testing conditions: data use is acceptable only with explicit consent. This consistency offers value for certain analytic tasks but exposes limits for advertising research that depends on emotional nuance and context-sensitive consumer insight.
Member Only Access
Large language models mirror human cognitive biases—but can those biases be guided? New ARF and MSI research reveals that while loss aversion remains deeply ingrained in AI responses, introducing persona information, such as demographics or personality traits, can increase variability and make outputs more nuanced. For advertisers and researchers, this opens the door to design strategic prompts that spark richer and more nuanced, human-like responses.
Member Only Access
How is the loss of digital identifiers reshaping advertising research? This guide, by the ARF Analytics Council, offers advertising researchers a deep dive into the privacy-first landscape, covering regulatory impacts, measurement challenges and practical identity solutions—from synthetic IDs to advanced modeling—to enable successful targeting and attribution in a fragmented ecosystem.
Member Only Access
The ARF tested whether generative AI can adopt executive personas and provide credible, role-specific strategies. This experiment highlights how AI performs when “thinking like” organizational leaders, its limitations in institutional logic and feasibility, and how human-in-the-loop feedback can refine outputs and create nuanced and worthwhile results.
Member Only Access
