The use of Large Language Models (LLMs) for writing has sparked controversy both among readers and writers. On one hand, writers are concerned that LLMs will deprive them of agency and ownership, and readers are concerned about spending their time on text generated by soulless machines. On the other hand, AI-assistance can improve writing as long as writers can conform to publisher policies, and as long as readers can be assured that a text has been verifed by a human. We argue that a system that captures the provenance of interaction with an LLM can help writers retain their agency, conform to policies, and communicate their use of AI to publishers and readers transparently. Thus we propose HaLLMark, a tool for visualizing the writer's interaction with the LLM. We evaluated HaLLMark with 13 creative writers, and found that it helped them retain a sense of control and ownership of the text.

Implicit biases and stereotypes are often pervasive in different forms of creative writing such as novels, screenplays, and children's books. To understand the kind of biases writers are concerned about and how they mitigate those in their writing, we conducted formative interviews with nine writers. The interviews suggested that despite a writer's best interest, tracking and managing implicit biases such as a lack of agency, supporting or submissive roles, or harmful language for characters representing marginalized groups is challenging as the story becomes longer and complicated. Based on the interviews, we developed DramatVis Personae (DVP), a visual analytics tool that allows writers to assign social identities to characters, and evaluate how characters and different intersectional social identities are represented in the story. To evaluate DVP, we first conducted think-aloud sessions with three writers and found that DVP is easy-to-use, naturally integrates into the writing process, and could potentially help writers in several critical bias identification tasks. We then conducted a follow-up user study with 11 writers and found that participants could answer questions related to bias detection more efficiently using DVP in comparison to a simple text editor.