Through past experiences deploying what we call usable ML (one step beyond explainable ML, including both explanations and other augmenting information) to real-world domains, we have learned three key lessons. First, many organizations are beginning to hire people who we call ``bridges'' because they bridge the gap between ML developers and domain experts, and these people fill a valuable role in developing usable ML applications. Second, a configurable system that enables easily iterating on usable ML interfaces during collaborations with bridges is key. Finally, there is a need for continuous, in-deployment evaluations to quantify the real-world impact of usable ML. Throughout this paper, we apply these lessons to the task of wind turbine monitoring, an essential task in the renewable energy domain. Turbine engineers and data analysts must decide whether to perform costly in-person investigations on turbines to prevent potential cases of brakepad failure, and well-tuned usable ML interfaces can aid with this decision-making process. Through the applications of our lessons to this task, we hope to demonstrate the potential real-world impact of usable ML in the renewable energy domain.

翻译：通过过往在真实领域部署我们称之为可用ML（超越可解释ML的一步，包括解释及其他增强信息）的经验，我们学到了三个关键教训。首先，许多组织开始雇用在ML开发者与领域专家之间架起桥梁的人员（我们称之为“桥梁”），这些人员在开发可用ML应用中发挥着宝贵作用。其次，一个可配置的系统能够实现与桥梁人员协作时快速迭代可用ML接口，这一点至关重要。最后，需要持续进行在部署环境中的评估，以量化可用ML的真实世界影响。在本文中，我们将这些经验教训应用于风力涡轮机监测这一可再生能源领域的关键任务。涡轮工程师和数据分析师需要决定是否对涡轮机进行昂贵的实地检查以防止潜在的刹车片故障，而经过优化的可用ML界面有助于这一决策过程。通过将这些经验应用于该任务，我们希望展示可用ML在可再生能源领域的潜在真实世界影响。