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Understanding the Development, Performance, Fairness, and Transparency of Machine Learning Models Used in Child Protection Prediction: A Systematic Review
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Reproducibility in machine‐learning‐based research: Overview, barriers, and drivers
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A Systematic Review of Sophisticated Predictive and Prescriptive Analytics in Child Welfare: Accuracy, Equity, and Bias
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Predictive Risk Modeling for Recurrence of Child Maltreatment Using Cases from the National Child Maltreatment Data System in Korea: Exploratory Data Analysis Using Data Mining Algorithm
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A systematic review of fairness in machine learning
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Inter-rater reliability of the Illinois Structured Decision Support Protocol
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Understanding the investigation-stage overrepresentation of First Nations children in the child welfare system: An analysis of the First Nations component of the Canadian Incidence Study of Reported Child Abuse and Neglect 2008
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Decision making in child protection: An international comparative study on maltreatment substantiation, risk assessment and interventions recommendations, and the role of professionals’ child welfare attitudes
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Predicting youth at high risk of aging out of foster care using machine learning methods
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Rates and predictors of child maltreatment re-perpetration against new victims and prior victims
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Leveraging machine learning for effective child maltreatment prevention: A case study of home visiting service assessments
inferredcites →https://doi.org/10.1016/j.childyouth.2011.04.005openalex-snapshot
Disentangling substantiation: The influence of race, income, and risk on the substantiation decision in child welfare
inferredcites →https://doi.org/10.1016/j.childyouth.2017.06.027openalex-snapshot
Risk assessment and decision making in child protective services: Predictive risk modeling in context
inferredcites →https://doi.org/10.1016/j.childyouth.2017.08.020openalex-snapshot
Predictive and prescriptive analytics, machine learning and child welfare risk assessment: The Broward County experience
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Early assessment of the risk of child welfare involvement for preventive purposes
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The pursuit of algorithmic fairness: On “Correcting” algorithmic unfairness in a child welfare reunification success classifier
inferredcites →https://doi.org/10.1016/j.childyouth.2023.107117openalex-snapshot
Predicting successful placements for youth in child welfare with machine learning
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Preserving data privacy in machine learning systems
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A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends
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Auditing fairness under unawareness through counterfactual reasoning
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An empirical characterization of fair machine learning for clinical risk prediction
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An Adaptive Machine Learning System for predicting recurrence of child maltreatment: A routine activity theory perspective
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Common errors of reasoning in child protection work
inferredcites →https://doi.org/10.1038/s41598-022-07939-1openalex-snapshot
A clarification of the nuances in the fairness metrics landscape
inferredcites →https://doi.org/10.1080/09503153.2020.1749584openalex-snapshot
Algorithmically Based Decision Support Tools: Skeptical Thinking about the Inclusion of Previous Involvement
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Reasoning and Bias: Heuristics in Safety Assessment and Placement Decisions for Children at Risk
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Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the ‘Black Box’ of Machine Learning
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Child maltreatment risk as a function of poverty and race/ethnicity in the USA
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Discrimination in Multiphase Systems: Evidence from Child Protection
inferredcites →https://doi.org/10.1097/ede.0b013e3181c30fb2openalex-snapshot
Assessing the Performance of Prediction Models
inferredcites →https://doi.org/10.1111/j.1365-2206.2011.00756.xopenalex-snapshot
Decision-making tools and the development of expertise in child protection practitioners: are we ‘just breeding workers who are good at ticking boxes’?
inferredcites →https://doi.org/10.1111/trf.18077openalex-snapshot
Privacy‐preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine
inferredcites →https://doi.org/10.1136/bmj-2023-078276openalex-snapshot
Developing clinical prediction models: a step-by-step guide
inferredcites →https://doi.org/10.1136/bmj-2023-078378openalex-snapshot
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
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The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
inferredcites →https://doi.org/10.1136/bmj.q902openalex-snapshot
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
inferredcites →https://doi.org/10.1145/3313831.3376229openalex-snapshot
A Human-Centered Review of Algorithms used within the U.S. Child Welfare System
inferredcites →https://doi.org/10.1145/3351095.3372864openalex-snapshot
On the apparent conflict between individual and group fairness
inferredcites →https://doi.org/10.1145/3411764.3445308openalex-snapshot
Soliciting Stakeholders’ Fairness Notions in Child Maltreatment Predictive Systems
inferredcites →https://doi.org/10.1145/3476089openalex-snapshot
A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare
inferredcites →https://doi.org/10.1145/3555101openalex-snapshot
Heterogeneity in Algorithm-Assisted Decision-Making: A Case Study in Child Abuse Hotline Screening
inferredcites →https://doi.org/10.1145/3593013.3594081openalex-snapshot
The Devil is in the Details: Interrogating Values Embedded in the Allegheny Family Screening Tool
inferredcites →https://doi.org/10.1145/3616865openalex-snapshot
Fairness in Machine Learning: A Survey
inferredcites →https://doi.org/10.1145/3710908openalex-snapshot
EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders
inferredcites →https://doi.org/10.1177/0002716220978200openalex-snapshot
A Practical Framework for Considering the Use of Predictive Risk Modeling in Child Welfare
inferredcites →https://doi.org/10.1371/journal.pmed.1001744openalex-snapshot
Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist
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Predictive risk modeling for child maltreatment detection and enhanced decision-making: Evidence from Danish administrative data
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The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
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Childhood Environmental Instabilities and Their Behavioral Implications: A Machine Learning Approach to Studying Adverse Childhood Experiences
inferredcites →https://doi.org/10.37419/jpl.v4.i5.2openalex-snapshot
Transparency and Fairness in Machine Learning Applications
inferredcites →https://doi.org/10.48550/arxiv.1710.03184openalex-snapshot
On Formalizing Fairness in Prediction with Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.2207.05855openalex-snapshot
A Conceptual Framework for Using Machine Learning to Support Child Welfare Decisions
inferredcites →https://doi.org/10.7326/m18-1376openalex-snapshot
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
inferredcites →https://doi.org/10.7326/m18-1377openalex-snapshot
PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration
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