A 2023 playback from the city of London’s Office of Technology and Innovation of recent efforts to improve data and technology innovation across the city.
City of London Office of Technology and Innovation (LOTI)
This article examines how applying a Racial Equity Framework reveals systemic inequities in the Earned Income Tax Credit (EITC) program, offering insights into barriers faced by marginalized communities and potential solutions.
Algorithmic impact assessments (AIAs) are an emergent form of accountability for organizations that build and deploy automated decision-support systems. This academic paper explores how to co-construct impacts that closely reflects harms, and emphasizes the need for input of various types of expertise and affected communities.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
This report offers a critical framework for designing algorithmic impact assessments (AIAs) by drawing lessons from existing impact assessments in areas like environment, privacy, and human rights to ensure accountability and reduce algorithmic harms.
This report analyzes lawsuits that have been filed within the past 10 years arising from the use of algorithm-driven systems to assess people’s eligibility for, or the distribution of, public benefits. It identifies key insights from the various cases into what went wrong and analyzes the legal arguments that plaintiffs have used to challenge those systems in court.
Our existing maze of family tax benefits — including the CTC, Earned Income Tax Credit (EITC), Child and Dependent Care Tax Credit (CDCTC), and head of household (HoH) filing status — has several structural deficiencies that make overhauling the system a prerequisite for any effort to boost support for families with children. The report offers several options for expanding and streamlining family tax benefits to address these issues.
This nine-minute video, produced after the completion of the TANF Data Collaborative (TDC) Pilot, features staff members from the California, Colorado, Minnesota, and Virginia TANF agencies reflecting on their challenges, accomplishments, and general experiences during the pilot. In particular, they describe their research questions and discuss building data capacity, integrating datasets, networking with other states, increasing collaboration between state and county agencies, learning new technical skills, and the benefits of being able to draw from diverse skillsets, all within the context of the TDC Pilot.
This article introduces ideas42, a non-profit behavioral research and design firm applying insights from behavioral sciences, including behavioral economics (BE), social psychology, and other disciplines, for social good. Their behavioral design methodology is human-centered, but it incorporates scientific insights and iterative testing, applying observations about how people choose and act from experimental academic research to inform qualitative research.
