Just Resource Allocation? How Algorithmic Predictions and Human Notions of Justice Interact

Kube, Amanda; Das, Sanmay; Fowler, Patrick J.; Vorobeychik, Yevgeniy

doi:10.1145/3490486.3538305

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Algorithmic systems are increasingly being deployed across several public sector organizations such as child welfare [29,75,109], public education [89,105], homeless services [80,81,91], criminal justice [128], unemployment services [64], and welfare benefits [44] to make high-stakes decisions about citizens' lives. For instance, algorithms are being used in the criminal justice system to determine sentencing length [52], allocate resources to neighborhoods [28], and predict recidivism [102].…”

Section: Algorithmic Decision-support(ads)mentioning

confidence: 99%

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

Haque,

Saxena,

Weathington

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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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) in this context. We conducted experiments and follow-up interviews with 60 participants, including community members, technical experts, and law enforcement agents (LEAs), to explore how lived experiences, technical knowledge, and domain expertise shape interactions with the ADS, impacting human-AI decision-making. Surprisingly, we found that domain experts (LEAs) often exhibited anchoring bias, readily accepting and engaging with the first crime map presented to them. Conversely, community members and technical experts were more inclined to engage with the tool, adjust controls, and generate different maps. Our findings highlight that all three stakeholders were able to provide critical feedback regarding AI design and use -community members questioned the core motivation of the tool, technical experts drew attention to the elastic nature of data science practice, and LEAs suggested redesign pathways such that the tool could complement their domain expertise. CCS CONCEPTS• Human-centered computing → Human-computer interaction (HCI); Empirical studies in HCI ; • Applied computing → Computing in government.

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Section: Algorithmic Decision-support(ads)mentioning

confidence: 99%

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

Haque,

Saxena,

Weathington

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…These algorithms were tasked to optimally allocate different resources (e.g., permanent supportive housing, rapid rehousing, counseling, psychiatric care) by matching unhoused individual's needs to available resources. RES algorithms in our datset also investigated how allocating resources based on different prioritization objectives (i.e., the most vulnerable or the most likely to take advantage of the services) can lead to different outcomes and impact our notions of fairness [66]. Unsurprisingly, RES algorithms have increased in prominence in the last three years (see Figure 3).…”

Section: Resource Allocation (Res)mentioning

confidence: 99%

A Human-Centered Review of Algorithms in Homelessness Research

Moon,

Guha

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Homelessness is a humanitarian challenge affecting an estimated 1.6 billion people worldwide. In the face of rising homeless populations in developed nations and a strain on social services, government agencies are increasingly adopting data-driven models to determine one's risk of experiencing homelessness and assigning scarce resources to those in need. We conducted a systematic literature review of 57 papers to understand the evolution of these decision-making algorithms. We investigated trends in computational methods, predictor variables, and target outcomes used to develop the models using a human-centered lens and found that only 9 papers (15.7%) investigated model fairness and bias. We uncovered tensions between explainability and ecological validity wherein predictive risk models (53.4%) unduly focused on reductive explainability while resource allocation models (25.9%) were dependent on unrealistic assumptions and simulated data that are not useful in practice. Further, we discuss research challenges and opportunities for developing human-centered algorithms in this area.CCS Concepts: • Human-centered computing → Human-computer interaction (HCI); Empirical studies in HCI ; • Applied computing → Computing in government.

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“…A very vulnerable household may not benefit much from an intensive intervention in some cases, but it may still be appropriate to prioritize them according to our values. These types of trade-offs are what caseworkers have to deal with on a daily basis (Kube et al 2022).…”

Section: Measuring Vulnerability and Potential Outcomesmentioning

confidence: 99%

Discretionary Trees: Understanding Street-Level Bureaucracy via Machine Learning

Pokharel,

Das,

Fowler

2024

AAAI

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Street-level bureaucrats interact directly with people on behalf of government agencies to perform a wide range of functions, including, for example, administering social services and policing. A key feature of street-level bureaucracy is that the civil servants, while tasked with implementing agency policy, are also granted significant discretion in how they choose to apply that policy in individual cases. Using that discretion could be beneficial, as it allows for exceptions to policies based on human interactions and evaluations, but it could also allow biases and inequities to seep into important domains of societal resource allocation. In this paper, we use machine learning techniques to understand street-level bureaucrats' behavior. We leverage a rich dataset that combines demographic and other information on households with information on which homelessness interventions they were assigned during a period when assignments were not formulaic. We find that caseworker decisions in this time are highly predictable overall, and some, but not all of this predictivity can be captured by simple decision rules. We theorize that the decisions not captured by the simple decision rules can be considered applications of caseworker discretion. These discretionary decisions are far from random in both the characteristics of such households and in terms of the outcomes of the decisions. Caseworkers typically only apply discretion to households that would be considered less vulnerable. When they do apply discretion to assign households to more intensive interventions, the marginal benefits to those households are significantly higher than would be expected if the households were chosen at random; there is no similar reduction in marginal benefit to households that are discretionarily allocated less intensive interventions, suggesting that caseworkers are using their knowledge and experience to improve outcomes for households experiencing homelessness.

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Just Resource Allocation? How Algorithmic Predictions and Human Notions of Justice Interact

Cited by 7 publications

References 12 publications

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

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

A Human-Centered Review of Algorithms in Homelessness Research

Discretionary Trees: Understanding Street-Level Bureaucracy via Machine Learning

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