Lighting Up or Dimming Down? Exploring Dark Patterns of LLMs in Co-Creativity

Abstract

Large language models (LLMs) are increasingly acting as collaborative writing partners, raising questions about their impact on human agency. In this exploratory work, we investigate five "dark patterns" in human-AI co-creativity -- subtle model behaviors that can suppress or distort the creative process: Sycophancy, Tone Policing, Moralizing, Loop of Death, and Anchoring. Through a series of controlled sessions where LLMs are prompted as writing assistants across diverse literary forms and themes, we analyze the prevalence of these behaviors in generated responses. Our preliminary results suggest that Sycophancy is nearly ubiquitous (91.7% of cases), particularly in sensitive topics, while Anchoring appears to be dependent on literary forms, surfacing most frequently in folktales. This study indicates that these dark patterns, often byproducts of safety alignment, may inadvertently narrow creative exploration and proposes design considerations for AI systems that effectively support creative writing.

Figures (4)

• Figure 1: Annotator agreement on dark pattern presence across prompts. Each cell represents the number of annotators (0--3) who marked a given dark pattern as present in a specific condition.
• Figure 2: Overall prevalence of five dark patterns across all prompts. Sycophancy is the most frequently observed behavior, followed by Anchoring and Loop of Death.
• Figure 3: Prevalence of dark patterns across literary forms. Anchoring is most prominent in folktales, while tone policing appears more often in structured genres like children's books.
• Figure 4: Dark pattern occurrence by concept category (benign vs. sensitive). Sycophancy is more frequent in sensitive prompts, whereas moralizing and looping behaviors are more common in benign content.