Fixing Inclusivity Bugs for Information Processing Styles and Learning Styles
This addresses gender bias and inclusivity issues in software design for diverse users, but appears incremental as it builds on existing studies and fixes.
The paper tackled the problem of software systems lacking support for cognitive diversity, which can embed gender biases due to differences in problem-solving styles, by collecting inclusivity fixes from three empirical studies to debug software for more inclusivity.
Most software systems today do not support cognitive diversity. Further, because of differences in problem-solving styles that cluster by gender, software that poorly supports cognitive diversity can also embed gender biases. To help software professionals fix gender bias "bugs" related to people's problem-solving styles for information processing and learning of new software we collected inclusivity fixes from three sources. The first two are empirical studies we conducted: a heuristics-driven user study and a field research industry study. The third is data that we obtained about a before/after user study of inclusivity bugs. The resulting seven potential inclusivity fixes show how to debug software to be more inclusive for diverse problem-solving styles.