Programmatically Identifying Cognitive Biases Present in Software Development
Amanda E. Kraft
Matthew Widjaja
Trevor M. Sands
Brad J. Galego
Mitigating bias in AI-enabled systems is a topic of great concern within the
research community. While efforts are underway to increase model
interpretability and de-bias datasets, little attention has been given to
identifying biases that are introduced by developers as part of the software
engineering process. To address this, we began developing an approach to
identify a subset of cognitive biases that may be present in development
artifacts: anchoring bias, availability bias, confirmation bias, and
hyperbolic discounting. We developed multiple natural language processing
(NLP) models to identify and classify the presence of bias in text
originating from software development artifacts.
cognitive bias, software engineering, natural language processing
DOI10.25080/majora-1b6fd038-00c