A Human-Centered Review of the Algorithms used within the U.S. Child
Welfare System
- URL: http://arxiv.org/abs/2003.03541v1
- Date: Sat, 7 Mar 2020 09:16:12 GMT
- Title: A Human-Centered Review of the Algorithms used within the U.S. Child
Welfare System
- Authors: Devansh Saxena, Karla Badillo-Urquiola, Pamela J. Wisniewski, and
Shion Guha
- Abstract summary: U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded.
Several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes.
We synthesize 50 peer-reviewed publications on computational systems used in CWS to assess how they were being developed, common characteristics of predictors used, as well as the target outcomes.
- Score: 17.161947795238916
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 human-centered algorithmic design approach, we synthesize 50
peer-reviewed publications on computational systems used in CWS to assess how
they were being developed, common characteristics of predictors used, as well
as the target outcomes. We found that most of the literature has focused on
risk assessment models but does not consider theoretical approaches (e.g.,
child-foster parent matching) nor the perspectives of caseworkers (e.g., case
notes). Therefore, future algorithms should strive to be context-aware and
theoretically robust by incorporating salient factors identified by past
research. We provide the HCI community with research avenues for developing
human-centered algorithms that redirect attention towards more equitable
outcomes for CWS.
Related papers
- Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition [70.60872754129832]
First NeurIPS competition on unlearning sought to stimulate the development of novel algorithms.
Nearly 1,200 teams from across the world participated.
We analyze top solutions and delve into discussions on benchmarking unlearning.
arXiv Detail & Related papers (2024-06-13T12:58:00Z) - Provably Efficient UCB-type Algorithms For Learning Predictive State
Representations [55.00359893021461]
The sequential decision-making problem is statistically learnable if it admits a low-rank structure modeled by predictive state representations (PSRs)
This paper proposes the first known UCB-type approach for PSRs, featuring a novel bonus term that upper bounds the total variation distance between the estimated and true models.
In contrast to existing approaches for PSRs, our UCB-type algorithms enjoy computational tractability, last-iterate guaranteed near-optimal policy, and guaranteed model accuracy.
arXiv Detail & Related papers (2023-07-01T18:35:21Z) - Examining risks of racial biases in NLP tools for child protective
services [78.81107364902958]
We focus on one such setting: child protective services (CPS)
Given well-established racial bias in this setting, we investigate possible ways deployed NLP is liable to increase racial disparities.
We document consistent algorithmic unfairness in NER models, possible algorithmic unfairness in coreference resolution models, and little evidence of exacerbated racial bias in risk prediction.
arXiv Detail & Related papers (2023-05-30T21:00:47Z) - A Gold Standard Dataset for the Reviewer Assignment Problem [117.59690218507565]
"Similarity score" is a numerical estimate of the expertise of a reviewer in reviewing a paper.
Our dataset consists of 477 self-reported expertise scores provided by 58 researchers.
For the task of ordering two papers in terms of their relevance for a reviewer, the error rates range from 12%-30% in easy cases to 36%-43% in hard cases.
arXiv Detail & Related papers (2023-03-23T16:15:03Z) - A Human-Centered Review of Algorithms in Decision-Making in Higher
Education [16.578096382702597]
We reviewed an extensive corpus of papers proposing algorithms for decision-making in higher education.
We found that the models are trending towards deep learning, and increased use of student personal data and protected attributes.
Despite the associated decrease in interpretability and explainability, current development predominantly fails to incorporate human-centered lenses.
arXiv Detail & Related papers (2023-02-12T02:30:50Z) - Exploring the effectiveness of surrogate-assisted evolutionary
algorithms on the batch processing problem [0.0]
This paper introduces a simulation of a well-known batch processing problem in the literature.
Evolutionary algorithms such as Genetic Algorithm (GA), Differential Evolution (DE) are used to find the optimal schedule for the simulation.
We then compare the quality of solutions obtained by the surrogate-assisted versions of the algorithms against the baseline algorithms.
arXiv Detail & Related papers (2022-10-31T09:00:39Z) - Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions.
For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions.
The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
arXiv Detail & Related papers (2022-05-13T21:09:41Z) - System Cards for AI-Based Decision-Making for Public Policy [5.076419064097733]
This work proposes a system accountability benchmark for formal audits of artificial intelligence-based decision-aiding systems.
It consists of 56 criteria organized within a four-by-four matrix composed of rows focused on (i) data, (ii) model, (iii) code, (iv) system, and columns focused on (a) development, (b) assessment, (c) mitigation, and (d) assurance.
arXiv Detail & Related papers (2022-03-01T18:56:45Z) - CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery [88.97076030698433]
We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery.
CIC explicitly incentivizes diverse behaviors by maximizing state entropy.
We find that CIC substantially improves over prior unsupervised skill discovery methods.
arXiv Detail & Related papers (2022-02-01T00:36:29Z) - A Framework of High-Stakes Algorithmic Decision-Making for the Public
Sector Developed through a Case Study of Child-Welfare [3.739243122393041]
We develop a cohesive framework of algorithmic decision-making adapted for the public sector.
We conduct a case study of the algorithms in daily use within a child-welfare agency.
We propose guidelines for the design of high-stakes algorithmic decision-making tools in the public sector.
arXiv Detail & Related papers (2021-07-07T21:24:35Z) - Safe Learning and Optimization Techniques: Towards a Survey of the State
of the Art [3.6954802719347413]
Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points.
A comprehensive survey of safe reinforcement learning algorithms was published in 2015, but related works in active learning and in optimization were not considered.
This paper reviews those algorithms from a number of domains including reinforcement learning, Gaussian process regression and classification, evolutionary algorithms, and active learning.
arXiv Detail & Related papers (2021-01-23T13:58:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.