This study investigates three challenges for developing machine learning-based self-service web apps for consumers. First, the authors argue that user research must accompany the development of ML-based products so that they better serve users’ needs at all stages of development. Second, they discuss the data sourcing dilemma in developing consumer-oriented ML-based apps and propose a way to solve it by implementing an interaction design that balances the workload between users and computers according to the ML component’s performance. To dynamically define the role of the user-in-the-loop, the authors monitor user success and ML performance over time. Finally, they propose a lightweight typology of ML-based systems to assess the generalizability of the findings to other ML use cases.
The case study uses a newly developed web application that allows consumers to analyze their heating bills for potential energy and cost savings. Based on domain-specific data values extracted from user-provided document images, an assessment of potential savings is derived and reported back to the user.
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