
This lightning talk will discuss the current efforts to identify and analyze the possibility of language bias in documents, exams, technical interviews, and actual interviews used in computer science as contributing factors in the continued lack of participation/success of underrepresented populations in computers science despite extensive efforts to develop a more inclusive environment. Although many factors have been examined while attempting to explain the continued lack of participation/success of under-represented populations in computer science, language bias in the very instruments that are used to measure success and entry does not seem to be among the forefront of these endeavors. This work is inspired by previous work done to examine biased language based upon neutral point of view (NPOV) revisions done on Wikipedia pages and seeks to apply similar methodologies to computer science instruments of evaluation in order to determine if there may indeed be a subtle and unconscious language bias within them.
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
