Transparency in Qualitative Research: Increasing Fairness in the CHI Review Process

Transparency in process and its reporting is paramount for establishing the rigor of qualitative studies. However, the CHI conference receives submissions with varying levels of transparency and oftentimes, papers that are more transparent can be inadvertently subjected to more scrutiny in the review process, raising issues of fairness. In this panel, we bring together researchers with diverse qualitative work experiences to present examples of transparency-related initiatives and their corresponding review responses. We aim to work towards setting standards for transparent reporting in qualitative-work submissions and increasing fairness in the review process. We focus on the challenges in achieving transparency in qualitative research and current workarounds to overcome frictions in the reviewing process through engaging discussions involving panelists and the audience.


Poorna Talkad Sukumar, Ignacio Avellino, Christian Remy, Michael A. DeVito, Tawanna R. Dillahunt, Joanna McGrenere, and Max L. Wilson. 2020. Transparency in Qualitative Research: Increasing Fairness in the CHI Review Process. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (CHI EA ’20). Association for Computing Machinery, New York, NY, USA, 1–6. DOI: