Investigating How to Design Inclusive Data-Driven Systems for Diverse User Groups

With the advance of logging technologies, various data-driven systems utilizing a vast amount of data are being developed and used in our lives. By analyzing data and users' usage patterns, data-driven systems are designed so that a typical user can better use the system. However, some users perceive and use data-driven systems differently from that of the majority of users. This leads to digital inequalities leaving out non-typical user groups from fully utilizing data-driven systems. My doctoral research suggests that there are three parts of data-driven systems—data collection, model, and interface—that require diverse user groups to be considered. Specifically, my prior work understands how (1) users with high concerns about privacy during data collection [11], (2) users with low performance of the model, and (3) users with different interface usage patterns use the data-driven systems and what kind of difficulties they face [10] in various contexts such as automatic personality assessment in workplaces, automatic speech recognition, and video platforms for learning. For future work, I will investigate how a data-driven system should be designed to enable better user experience for various user groups by suggesting a user-adaptive automatic speech recognition system.