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.

Sketches are much more than marks on paper; they play a key role for designers both in ideation and problem-solving as well as in communication with other designers. Thus, the act of sketching is often enriched with annotations, references, and physical actions, such as gestures or speech—all of which constitute meta-data about the designer’s reasoning. Conventional paper-based design notebooks cannot capture this rich metadata, but digital design notebooks can. To understand how and what data to capture, we conducted an observational study of design practitioners where they explore design solutions for a set of problems. We recorded and coded their sketching and non-sketching actions that reflect their exploration of the design space. We then categorized the captured meta-data and mapped observed physical actions to design intent. These findings inform the creation of future digital design notebooks that can better capture designers’ reasoning during sketching.