This Perspective explores potential policy challenges ahead as artificial intelligence (AI) becomes more central in the private, commercial, and public spheres. It explores the implications of AI prevalence on two key policy-relevant areas: security and employment. Our focus was on highlighting the potential vulnerabilities and inequities that the use of AI imposes on these two dimensions of society.
RAND has contributed more to thinking about how to deal with the longer-range future than any other organization. The methodological work at RAND started in the late 1950s and 1960s with the developments of the Delphi method and scenario analysis. This work continued sporadically through the 1970s and 1980s—and since the 1990s—computational technology has enabled new futures methodologies such as Robust Decision Making and Scenario Discovery.
RAND is again leading the way in putting these methods to use in dealing with the challenges of the future. Below is a list of publications that exemplifies RAND's work on Robust Decision Making methodology and applications.
Demonstrating the Applicability of a Robust Decision Making (RDM) to Conservation Decision-Making Under Uncertain Future Climate: Pilot Study Using the Northern Pygmy Salamander (Desmognathus Organi) October 3, 2017
This study suggests initial ideas for managing climate uncertainty in conservation planning. Differences with previous RDM applications include focus on finer scale geography and significantly more uncertainty in the system (species response) model.
This report provides an independent study of how the stormwater problem in the Pittsburgh, Pennsylvania metropolitan region could grow with future climate, land use, or population change, and discusses potential long-term solutions using new analytical approaches developed by RAND. The analysis provides a baseline of scientific information intended to support ongoing regional coordination around stormwater management and water-quality planning.
Machine learning algorithms and artificial intelligence influence many aspects of life today and have gained an aura of objectivity and infallibility. The use of these tools introduces a new level of risk and complexity in policy. This report illustrates some of the shortcomings of algorithmic decisionmaking, identifies key themes around the problem of algorithmic errors and bias, and examines some approaches for combating these problems.
Testing the Scenario Hypothesis: An Experimental Comparison of Scenarios and Forecasts for Decision Support in a Complex Decision Environment March 16, 2017
Decision support tools are known to influence and facilitate decisionmaking through the thoughtful construction of the decision environment. However, little research has empirically evaluated the effects of using scenarios and forecasts.
Evaluation of the Jinan City Water Ecological Development Implementation Plan and Recommendations for Improvement March 9, 2017
RAND evaluated potential effects of uncertain projections of demand and climate change on the ability of the Jinan Municipal Water Resources Bureau to meet its long-term water resources goals. This document describes RAND's approach and results, including development of a mathematical simulation model of the Jinan water supply system and analysis of the system's performance if new strategies and investments were to be implemented.
Urban Responses to Climate Change: Framework for Decisionmaking and Supporting Indicators December 7, 2016
This report presents a decisionmaking framework and supporting indicators to guide urban responses to climate change based on principles of risk governance, along with considerations of institutional arrangements and their economic and social context. The framework emphasizes identification of indicators of capacities and processes to implement, adapt, and transform policies, institutions, financing, and other actions to affect change.
Improving Decision Support for Infectious Disease Prevention and Control: Aligning Models and Other Tools with Policymakers' Needs July 12, 2016
This report provides an overview of decision-support tools, including models and nonmodeling approaches, to inform both modelers and policymakers. It offers ways to use these tools to help address real-world policy questions related to infectious disease prevention, detection, control, and response and recommends best practices for technical experts and policymakers to collaborate in developing and using the tools.