This guide discusses general characteristics shared by organizations that have successfully created accessible content, and includes case studies that showcase characteristics of successful accessible content teams.
Teams crafting policy inside and outside government can use the assessment to center their policy-making activities around those most impacted by their proposed programs and policy ideas.
This reporting explores how algorithms used to screen prospective tenants, including those waiting for public housing, can block renters from housing based on faulty information.
This article analyzes the strategic use of public policy as a tool for reshaping public opinion. Though progressive revisionists in the 1990s argued that reforming welfare could produce a public more willing to invest in anti-poverty efforts, welfare reform in the 1990s did little to shift public opinion. This study investigates the general conditions under which mass feedback effects should be viewed as more or less likely.
This book is an in-depth exploration of federal programs and controversial legislation demonstrating that administrative burden has long existed in policy design, preventing citizens from accessing fundamental rights. Further discussion of how policymakers can minimize administrative burden to reduce inequality, boost civic engagement, and build an efficient state.
This policy brief outlines how improved data sharing between federal agencies, state and local governments, and institutions can leverage existing data from other benefits programs to streamline eligibility processes and benefits uptake for the Affordable Connectivity Program (ACP) and other programs.
An interview with Wendy De La Rosa, assistant professor at the Wharton School at the University of Pennsylvania. De La Rosa discusses how the concept of “psychological ownership” can encourage people to take up benefits they are eligible for.
This article examines how administrative burdens in U.S. social safety net programs have changed over the past 30 years, showing that while average burdens have declined, inequality in who faces these burdens has grown.
The ANNALS of the American Academy of Political and Social Science
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
This retrospective looks at the way the NYCOpportunity initiative worked across City government, partnering with agencies to initiate new approaches and enhance city practices. It also highlights key areas of focus for the NYC Opportunity team between 2014 and 2021.
It is frequently assumed that when rules are implemented as code, a rules engine is necessary. However, it is possible for policy people and engineers to effectively work together to code logic that drives technological system without needing a mediating rules engine at all.
