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The Promises and Perils of using LLMs for Effective Public Services

Governments are the primary providers of essential public services and are responsible for delivering them effectively. In high-stakes decision-making domains such as child welfare (CW), agencies must protect children without unnecessarily prolonging a family’s engagement with the system. With growing optimism around…

Erina Seh-Young Moon et al.University of Toronto

When Metrics Mislead: Parents’ Lived Realities in the Public Safety Net

Public-sector agencies increasingly rely on data and information systems to demonstrate that they “support” families. Yet, the metrics that stand in for support are often misaligned with how they are lived. We draw on interviews with 75 parents involved in the child welfare system (CWS)—an entry point into the broader…

Devansh Saxena et al.University of Wisconsin - Madison

Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks

Data scientists often formulate predictive modeling tasks involving fuzzy, hard-to-define concepts, such as the "authenticity'' of student writing or the "healthcare need'' of a patient. Yet the process by which data scientists translate fuzzy concepts into a concrete, proxy target variable remains poorly understood.…

Luke Guerdan et al.Carnegie Mellon University

Generating and Sharing Knowledge

Making the Right Thing: Bridging HCI and Responsible AI in Early-Stage AI Concept Selection

AI projects often fail due to financial, technical, ethical, or user acceptance challenges—failures frequently rooted in early-stage decisions. While HCI and Responsible AI (RAI) research emphasize this, practical approaches for identifying promising concepts early remain limited. Drawing on Research through Design, t…

Ji-Youn Jung et al.Carnegie Mellon University

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AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development

AI systems are often introduced with high expectations, yet many fail to deliver, resulting in unintended harm and missed opportunities for benefit. We frequently observe significant "AI Mismatches", where the system’s actual performance falls short of what is needed to ensure safety and co-create value. These mismatc…

Devansh Saxena et al.University of Wisconsin - Madison

The Datafication of Care in Public Homelessness Services

Homelessness systems in North America adopt coordinated data-driven approaches to efficiently match support services to clients based on their assessed needs and available resources. AI tools are increasingly being implemented to allocate resources, reduce costs and predict risks in this space. In this study, we condu…

Erina Seh-Young Moon et al.University of Toronto

Are We Asking the Right Questions?: Designing for Community Stakeholders’ Interactions with AI in Policing

Research into recidivism risk prediction in the criminal justice system has garnered significant attention from HCI, critical algorithm studies, and the emerging field of human-AI decision-making. This study focuses on algorithmic crime mapping, a prevalent yet underexplored form of algorithmic decision support (ADS)…

Md. Romael Haque et al.Marquette University

Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child Welfare

Risk assessment algorithms are being adopted by public sector agencies to make high-stakes decisions about human lives. Algorithms model “risk” based on individual client characteristics to identify clients most in need. However, this understanding of risk is primarily based on easily quantifiable risk factors that pr…

Devansh Saxena et al.University of Wisconsin - Madison

Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis

Caseworkers are trained to write detailed narratives about families in Child-Welfare (CW) which informs collaborative high-stakes decision-making. Unlike other administrative data, these narratives offer a more credible source of information with respect to workers’ interactions with families as well as underscore the…

Devansh Saxena et al.University of Wisconsin - Madison

A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare

Algorithms have permeated throughout civil government and society, where they are being used to make high-stakes decisions about human lives. In this paper, we first develop a cohesive framework of algorithmic decision-making adapted for the public sector (ADMAPS) that reflects the complex socio-technical interactions…

Devansh Saxena et al.University of Wisconsin - Madison

Algorithms and Decision Making

Methods for Generating Typologies of Non/use

Prior studies of technology non-use demonstrate the need for approaches that go beyond a simple binary distinction between users and non-users. This paper proposes a set of two different methods by which researchers can identify types of non/use relevant to the particular sociotechnical settings they are studying. The…

Devansh Saxena et al.University of Wisconsin - Madison

Technological Inclusion and Non/Use

A Human-Centered Review of Algorithms used within the U.S. Child Welfare System

The U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded. To overcome this limitation, several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes. Using a hum…

Devansh Saxena et al.University of Wisconsin - Madison

Paper Title	Authors	Research Topics
The Promises and Perils of using LLMs for Effective Public Services Governments are the primary providers of essential public services and are responsible for delivering them effectively. In high-stakes decision-making domains such as child welfare (CW), agencies must protect children without unnecessarily prolonging a family’s engagement with the system. With growing optimism around…	EM Erina Seh-Young Moon et al.University of Toronto
When Metrics Mislead: Parents’ Lived Realities in the Public Safety Net Public-sector agencies increasingly rely on data and information systems to demonstrate that they “support” families. Yet, the metrics that stand in for support are often misaligned with how they are lived. We draw on interviews with 75 parents involved in the child welfare system (CWS)—an entry point into the broader…	DS Devansh Saxena et al.University of Wisconsin - Madison
Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks Data scientists often formulate predictive modeling tasks involving fuzzy, hard-to-define concepts, such as the "authenticity'' of student writing or the "healthcare need'' of a patient. Yet the process by which data scientists translate fuzzy concepts into a concrete, proxy target variable remains poorly understood.…	LG Luke Guerdan et al.Carnegie Mellon University	Generating and Sharing Knowledge
emoji_events Making the Right Thing: Bridging HCI and Responsible AI in Early-Stage AI Concept Selection AI projects often fail due to financial, technical, ethical, or user acceptance challenges—failures frequently rooted in early-stage decisions. While HCI and Responsible AI (RAI) research emphasize this, practical approaches for identifying promising concepts early remain limited. Drawing on Research through Design, t…	JJ Ji-Youn Jung et al.Carnegie Mellon University
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development AI systems are often introduced with high expectations, yet many fail to deliver, resulting in unintended harm and missed opportunities for benefit. We frequently observe significant "AI Mismatches", where the system’s actual performance falls short of what is needed to ensure safety and co-create value. These mismatc…	DS Devansh Saxena et al.University of Wisconsin - Madison
emoji_events The Datafication of Care in Public Homelessness Services Homelessness systems in North America adopt coordinated data-driven approaches to efficiently match support services to clients based on their assessed needs and available resources. AI tools are increasingly being implemented to allocate resources, reduce costs and predict risks in this space. In this study, we condu…	EM Erina Seh-Young Moon et al.University of Toronto
Are We Asking the Right Questions?: Designing for Community Stakeholders’ Interactions with AI in Policing Research into recidivism risk prediction in the criminal justice system has garnered significant attention from HCI, critical algorithm studies, and the emerging field of human-AI decision-making. This study focuses on algorithmic crime mapping, a prevalent yet underexplored form of algorithmic decision support (ADS)…	MH Md. Romael Haque et al.Marquette University
emoji_events Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child Welfare Risk assessment algorithms are being adopted by public sector agencies to make high-stakes decisions about human lives. Algorithms model “risk” based on individual client characteristics to identify clients most in need. However, this understanding of risk is primarily based on easily quantifiable risk factors that pr…	DS Devansh Saxena et al.University of Wisconsin - Madison
Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis Caseworkers are trained to write detailed narratives about families in Child-Welfare (CW) which informs collaborative high-stakes decision-making. Unlike other administrative data, these narratives offer a more credible source of information with respect to workers’ interactions with families as well as underscore the…	DS Devansh Saxena et al.University of Wisconsin - Madison
emoji_events A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare Algorithms have permeated throughout civil government and society, where they are being used to make high-stakes decisions about human lives. In this paper, we first develop a cohesive framework of algorithmic decision-making adapted for the public sector (ADMAPS) that reflects the complex socio-technical interactions…	DS Devansh Saxena et al.University of Wisconsin - Madison	Algorithms and Decision Making
Methods for Generating Typologies of Non/use Prior studies of technology non-use demonstrate the need for approaches that go beyond a simple binary distinction between users and non-users. This paper proposes a set of two different methods by which researchers can identify types of non/use relevant to the particular sociotechnical settings they are studying. The…	DS Devansh Saxena et al.University of Wisconsin - Madison	Technological Inclusion and Non/Use
emoji_events A Human-Centered Review of Algorithms used within the U.S. Child Welfare System The U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded. To overcome this limitation, several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes. Using a hum…	DS Devansh Saxena et al.University of Wisconsin - Madison