Designing a Mobile User Interface for Crowdsourced Verification of Datasets

Abstract: During the last decade machine learning has spread rapidly in computer science and beyond, and a central issue for machine learning is data quality. This study was carried out in the intersection of business and Human-Computer Interaction, examining how an interface may be developed for crowdsourced verification of datasets.The interface is developed for efficiency and enjoyability through research on areas such as usability, information presentation models and gamification. The interface was developed iteratively, drawing from needs of potential users as well as the machine learning industry. More specifically, the process involved a literature study, expert interviews, a user survey on the Kenyan market and user tests. The study was divided into a conceptual phase and a design phase, each constituting a clearly bounded part of the study with a prototype being developed in each stage. The results of this study give an interesting insight on what usability factors are important when designing a practical tool-type mobile application, while balancing efficiency and enjoyability. The resulting novel interface indicated on a more effective performance than a conventional grid layout and is more enjoyable to use according to the users. In addition, the ‘rapid serial visual presentation’ can be deemed a well-functioning model for tool-type mobile applications which require a high amount of binary decisions on short time. The study highlights the importance of iterative, user-driven processes, allowing a new innovation or idea to merge with the needs and skills of users. The results may be of interest to anyone developing tool-type mobile applications and certainly if binary decision making on images is central.