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 article explores how legal documents can be treated like software programs, using methods like software testing and mutation analysis to enhance AI-driven statutory analysis, aiding legal decision-making and error detection.
This report documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
This is the summary version of a report that documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
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 paper analyzes the unique challenges of conducting participatory design in large-scale public projects, focusing on stakeholder management, fostering engagement, and integrating participatory methods into institutional transformation.
This paper examines three key questions in participatory HCI: who initiates, directs, and benefits from user participation; in what forms it occurs; and how control is shared with users, while addressing conceptual, ethical, and pragmatic challenges, and suggesting future research directions.
This video documents the Digital Benefits Network's Digital Identity Community of Practice launch, covering mission review, 2025 goals, California authentication innovations, and peer networking for equitable and effective digital identity in public benefits.
This report explores key questions that a focus on disability raises for the project of understanding the social implications of AI, and for ensuring that AI technologies don’t reproduce and extend histories of marginalization.
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.
