Sensors and control technologies are being deployed at unprecedented levels in both urban and rural water environments. Because sensor networks and control allow for higher-resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented precision of impacts, both positive and negative. Likewise, humans will continue to cede direct decision-making powers to decision-support technologies, e.g. data algorithms. Systems will have ever-greater potential to effect human lives, and yet, humans will be distanced from decisions. Combined these trends challenge water resources management decision-support tools to incorporate the concepts of ethical and normative expectations. Toward this aim, we propose the Water Ethics Web Engine (WE)², an integrated and generalized web framework to incorporate voting-based ethical and normative preferences into water resources decision support. We demonstrate this framework with a ‘proof-of-concept’ use case where decision models are learned and deployed to respond to flooding scenarios. Findings indicate that the framework can capture group ‘wisdom’ within learned models to use in decision making. The methodology and ‘proof-of-concept’ system presented here are a step toward building a framework to engage people with algorithmic decision making in cases where ethical preferences are considered. We share our framework and its cyber components openly with the research community.

传感器和控制技术正在城市和农村水环境中以空前的水平部署。因为传感器网络和控制允许在时间和空间上进行更高分辨率的监视和决策，所以控制的更大程度的离散化将使影响的积极性和消极性达到前所未有的精确度。同样，人类将继续把直接的决策权移交给决策支持技术，例如数据算法。系统将具有更大的影响人类生活的潜力，但是，人类将远离决策。结合这些趋势，挑战水资源管理决策支持工具以纳入道德和规范性期望的概念。为了实现这一目标，我们提出了水伦理网络引擎（WE）²，一个集成的通用网络框架，可将基于投票的道德和规范偏好纳入水资源决策支持中。我们以“概念验证”用例演示了该框架，在该用例中学习并部署了决策模型以应对泛滥情况。研究结果表明，该框架可以在学习的模型中捕获小组的“智慧”，以用于决策。这里介绍的方法论和“概念验证”系统是朝着建立框架的方向迈出的一步，这种框架可以在考虑到伦理偏好的情况下使人们参与算法决策。我们与研究社区公开共享我们的框架及其网络组件。