论文标题
以人为中心的模型监控
A Human-Centric Take on Model Monitoring
论文作者
论文摘要
预测模型越来越多地用于在医疗保健,金融和政策等高风险领域做出各种结果决策。确保这些模型做出准确的预测,对数据的变化,不依赖虚假特征,并且不会过分区分少数群体,这变得至关重要。为此,最近的文献提出了几种涵盖各个领域的方法,例如解释性,公平性和鲁棒性。当这种方法迎合对用户对模型的理解时,需要以人为本。但是,一旦部署了监测机器学习的需求和挑战,就存在研究差距。为了填补这一差距,我们对13位从业人员进行了访谈研究,他们在部署ML模型并与跨越领域的客户互动的交汇处,例如金融服务,医疗保健,招聘,在线零售,计算广告和对话助理。我们确定了在现实世界应用中对模型监控的各种挑战和要求。具体而言,我们发现模型监视系统的需求和挑战是阐明监测观察结果对结果的影响。此外,此类见解必须是可行的,可靠的,可针对特定领域的用例定制,并认知体贴以避免信息过载。
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.