  Predictive models are increasingly used to make various consequential
decisions in high-stakes domains such as healthcare, finance, and policy. It
becomes critical to ensure that these models make accurate predictions, are
robust to shifts in the data, do not rely on spurious features, and do not
unduly discriminate against minority groups. To this end, several approaches
spanning various areas such as explainability, fairness, and robustness have
been proposed in recent literature. Such approaches need to be human-centered
as they cater to the understanding of the models to their users. However, there
is a research gap in understanding the human-centric needs and challenges of
monitoring machine learning (ML) models once they are deployed. To fill this
gap, we conducted an interview study with 13 practitioners who have experience
at the intersection of deploying ML models and engaging with customers spanning
domains such as financial services, healthcare, hiring, online retail,
computational advertising, and conversational assistants. We identified various
human-centric challenges and requirements for model monitoring in real-world
applications. Specifically, we found the need and the challenge for the model
monitoring systems to clarify the impact of the monitoring observations on
outcomes. Further, such insights must be actionable, robust, customizable for
domain-specific use cases, and cognitively considerate to avoid information
overload.
