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).
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.
2022 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization
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.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
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.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. This academic study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets.
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 academic article develops a framework for evaluating whether and how automated decision-making welfare systems introduce new harms and burdens for claimants, focusing on an example case from Germany.
This study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
Automated decision systems (ADS) are increasingly used in government decision-making but lack clear definitions, oversight, and accountability mechanisms.
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 report puts forth an anti-racist reimagining of Medicaid and CHIP that actively reckons with the racist history of the Medicaid program and offers principles and recommendations that capitalize on the transformative potential of the programs. The principles center the voices and agency of program participants and prioritize direct community involvement at all stages of the policy process.
This brief analyzes the current state of federal and state government communication around benefits eligibility rules and policy and how these documents are being tracked and adapted into code by external organizations. This work includes comparisons between coded examples of policy and potential options for standardizing code based on established and emerging data standards, tools, and frameworks.
