DelphAI: A human-centered approach to time-series forecasting.

When applying machine learning (ML) based techniques to time-series forecasting applications, there are many domain-specific considerations that can be integrated into model development to improve the likelihood of successful real-world translation. A human-centered approach, that involves end-users, has the potential to address commonly cited concerns such as algorithmic trust, explainability, and fairness. We present the DelphAI framework as an example of a practical human-centered approach to ML-based time-series forecasting for applications where end-users have little familiarity with ML techniques. The proposed socio-technical methodology incorporates essential domain knowledge through stakeholder participation into the development of predictive models. We advocate that the application of user-centered design principles can improve downstream translation and address other ethical concerns associated with ML-based forecasting.