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 guide consolidates learning and spotlights principles, insights, and emerging practices to guide municipal leaders and public-private partnerships interested in designing basic income programs that are ethical, equitable, rigorous, informative, and consequential for local, state and national policymaking.
Hear perspectives on topics including centering beneficiaries and workers in new ways, digital service delivery, digital identity, and automation.This video was recorded at the Digital Benefits Conference (BenCon) on June 14, 2023.
The Electronic Privacy Information Center (EPIC) emphasizes the necessity of adopting broad regulatory definitions for automated decision-making systems (ADS) to ensure comprehensive oversight and protection against potential harms.
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.
Study by the Director of the Office of Management and Budget assessing methods for determining whether agency policies and actions create or exacerbate barriers to full and equal participation by eligible individuals. This study followed the Executive Order on racial equity.
