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Time to Define the Objectively Reasonable Officer: How Maryland\u27s Use of Force Statute Supplies Measureable Standards to Protect Fourth Amendment Rights
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To Fight the Battle, First You Need Warriors: Edward Garrison Draper, Everett Waring, and the Quest for Maryland\u27s First Black Lawyer
Get PDFHow Algorithm-Assisted Decision Making Is Influencing Environmental Law and Climate Adaptation
Get PDFAlgorithm-based decision tools in environmental law appear policy neutral but embody bias and hidden values that affect equity and democracy. In effect, algorithm-based tools are new fora for law and policymaking, distinct from legislatures and courts. In turn, these tools influence the development and implementation of environmental law and regulation. As a practical matter, there is a pressing need to understand how these automated decision-making tools interact with and influence law and policy. This Article begins this timely and critical discussion.
After introducing the challenge of adapting water and energy systems to climate change, this Article synthesizes prior multidisciplinary work on algorithmic decision making and modeling-informed governance—bringing together the works of early climate scientists and contemporary leaders in algorithmic decision making. From this synthesis, this Article presents a framework for analyzing how well these tools integrate principles of equity, including procedural and substantive fairness—both of which are essential to democracy. The framework evaluates how the tools handle uncertainty, transparency, and stakeholder collaboration across two attributes. The first attribute has to do with the model itself—specifically, how and whether existing law and policy are incorporated into these tools. These social parameters can be incorporated as inputs to the model or in the structure of the model, which determines its logic. The second attribute has to do with the modeling process— how and whether stakeholders and end-users collaborated in the model’s development.
The Article then applies this framework and compares two algorithm assisted decision-making tools currently in use for adapting water and energy systems to climate change
