REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
This addresses the problem of insufficient transparency in ML research for the research community, though it is incremental as it focuses on developing tools rather than a paradigm shift.
The paper tackles the lack of norms for disclosing limitations in machine learning research by developing REAL ML, a set of guided activities through an iterative design process with 30 researchers, aiming to improve scientific rigor and credibility.
Transparency around limitations can improve the scientific rigor of research, help ensure appropriate interpretation of research findings, and make research claims more credible. Despite these benefits, the machine learning (ML) research community lacks well-developed norms around disclosing and discussing limitations. To address this gap, we conduct an iterative design process with 30 ML and ML-adjacent researchers to develop and test REAL ML, a set of guided activities to help ML researchers recognize, explore, and articulate the limitations of their research. Using a three-stage interview and survey study, we identify ML researchers' perceptions of limitations, as well as the challenges they face when recognizing, exploring, and articulating limitations. We develop REAL ML to address some of these practical challenges, and highlight additional cultural challenges that will require broader shifts in community norms to address. We hope our study and REAL ML help move the ML research community toward more active and appropriate engagement with limitations.