Children in the public benefit system at risk of maltreatment: identification via predictive modeling.

By Rhema Vaithianathan, T. Maloney, Emily Putnam-Hornstein, Nan Jiang. 2013

105 citations recorded by the source corpus.

Subfields: Clinical Psychology, General Health Professions, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Clinical Psychology

1. Shakir Mohamed
2. Alexandra Chouldechova
3. Kristian Lum

In General Health Professions

1. Reuben Binns

In Sociology and Political Science

1. Luca Oneto
2. Silvia Chiappa
3. Reuben Binns
4. William S. Isaac
5. Solon Barocas
6. Michael Veale
7. Kristian Lum
8. Alexandra Chouldechova

Citations in the corpus

• A Causal Bayesian Networks Viewpoint on Fairness (2018, neutral): “…scenarios that have serious implications for individuals and society, including financial lending [10, 44], hiring [8, 27], online advertising [26, 38], pretrial and immigration detention [5, 40], child maltreatment screening [13, 45], health care [18, 31], and social services [1, 22].”
• Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, methodology): “In another case of beta-testing, the deployment of predictive algorithms for child welfare interventions by the New Zealand government initially targeted Māori, the indigenous people of New Zealand, who have long experienced institutional racism (Vaithianathan et al. 2013; Gavighan et al. 2019).”
• Fairness in Machine Learning (2020, background): “Machine Learning (ML) is increasingly used in a wide range of decision-making scenarios that have serious implications for individuals and society, including financial lending [20, 120], hiring [17, 77], online advertising [72, 150], pretrial and immigration detention [10, 158], child maltreatment s”
• A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions (2018, background): “PRM has been used previously in health and hospital settings (Panattoni et al., 2011; Billings et al., 2012) and has been suggested as a potentially useful tool that could be translated into child protection settings (Vaithianathan et al., 2013).”
• Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data (2017, background): “…anticipating the geospatial distribution of crime (Azavea, 2015; Perry et al., 2013; Wetenschappelijke Raad voor het Regeringsbeleid (WRR), 2016), the need for child protection (Vaithianathan et al., 2013) and the detection of tax fraud (Khwaja et al., 2011; Sharma and Kumar Panigrahi, 2012).”
• Algorithmic Fairness: Choices, Assumptions, and Definitions (2021, background): “…and they increasingly figure into decisions regarding pretrial detention (Angwin et al. 2016, Dieterich et al. 2016, Larson et al. 2016), immigration detention (Koulish 2016), child maltreatment screening (Vaithianathan et al.2013,Chouldechova et al.2018,Eubanks 2018),public health (Mayor’s Off.”