Produced By: Academic
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New Jersey’s Worker-centered Approach to Improving the Administration of Unemployment Insurance
This paper describes the policy choices, business practices, and technology innovations that the State of New Jersey is employing to ensure that the right people get benefits — accurately and on time.
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What the Digital Benefits Network is Reading on Automation
In this piece, the Digital Benefits Network shares several sources—from journalistic pieces, to reports and academic articles—we’ve found useful and interesting in our reading on automation and artificial intelligence.
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Digital Benefits Wishlist
The Digital Benefits Wishlist collects on tools, resources, or policy changes needed to improve benefits delivery.
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The Wait List as Redistributive Policy: Access and Burdens in the Subsidized Childcare System
In the article, researchers examines how administrative burdens in waitlist management for subsidized childcare in Massachusetts have led to significant reductions in the number of families awaiting assistance, potentially obscuring the true extent of unmet need.
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Lightning Talks at BenCon 2023
This video was recorded at the Digital Benefits Conference (BenCon) at Georgetown University on June 14, 2023.
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Administrative Burdens and Economic Insecurity Among Black, Latino, and White Families
This study investigates how administrative burdens influence differential receipt of income transfers after a family member loses a job, looking at Unemployment Insurance, Temporary Assistance for Needy Families, and the Supplemental Nutrition Assistance Program.
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“I Used to Get WIC . . . But Then I Stopped”: How WIC Participants Perceive the Value and Burdens of Maintaining Benefits
This study examines how individuals assess administrative burdens and how these views change over time within the context of the Special Supplemental Nutrition Assistance Program for Women, Infants, and Children (WIC).
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Evaluating Facial Recognition Technology: A Protocol for Performance Assessment in New Domains
In May 2020, Stanford's HAI hosted a workshop to discuss the performance of facial recognition technologies that included leading computer scientists, legal scholars, and representatives from industry, government, and civil society. The white paper this workshop produced seeks to answer key questions in improving understandings of this rapidly changing space.
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Domain Shift and Emerging Questions in Facial Recognition Technology
This policy brief offers recommendations to policymakers relating to the computational and human sides of facial recognition technologies based on a May 2020 workshop with leading computer scientists, legal scholars, and representatives from industry, government, and civil society
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A Human Rights-Based Approach to Responsible AI
This paper argues that a human rights framework could help orient the research on artificial intelligence away from machines and the risks of their biases, and towards humans and the risks to their rights, helping to center the conversation around who is harmed, what harms they face, and how those harms may be mitigated.
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Who Audits the Auditors? Recommendations from a Field Scan of the Algorithmic Auditing Ecosystem
Through a field scan, this paper identifies emerging best practices as well as methods and tools that are becoming commonplace, and enumerates common barriers to leveraging algorithmic audits as effective accountability mechanisms.
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The Privacy-Bias Tradeoff: Data Minimization and Racial Disparity Assessments in U.S. Government
An emerging concern in algorithmic fairness is the tension with privacy interests. Data minimization can restrict access to protected attributes, such as race and ethnicity, for bias assessment and mitigation. This paper examines how this “privacy-bias tradeoff” has become an important battleground for fairness assessments in the U.S. government and provides rich lessons for resolving these tradeoffs.