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.

在可再生能源领域的风力涡轮机监测任务中，通过对我们所说的可用机器学习（超越可解释机器学习的一步，包括解释和其他增强信息）的部署经验，我们得出了三个关键教训：聘请我们称之为 “桥梁” 的人员填补机器学习开发者和领域专家之间的鸿沟，配置灵活的系统使得在与这些桥梁的合作中可以轻松迭代可用机器学习界面，需要在部署过程中进行持续不断的评估以量化可用机器学习的实际影响。希望通过将这些教训应用于此任务来展示可用机器学习在可再生能源领域中的潜在实际影响。