Next Article in Journal
Gender Concerns When Noah the Economist Ranks Biodiversity Protection Policies
Previous Article in Journal
Information Disorder and Self-Regulation in Europe: A Broader Non-Economistic Conception of Self-Regulation
Open AccessArticle

Algorithmic Justice in Child Protection: Statistical Fairness, Social Justice and the Implications for Practice

Social and Community Work Programme, School of Social Science, University of Otago, Dunedin 9054, Aotearoa, New Zealand
Soc. Sci. 2019, 8(10), 281; https://doi.org/10.3390/socsci8100281
Received: 5 August 2019 / Revised: 18 September 2019 / Accepted: 26 September 2019 / Published: 8 October 2019
(This article belongs to the Special Issue Critical Debates and Developments in Child Protection)
Algorithmic tools are increasingly used in child protection decision-making. Fairness considerations of algorithmic tools usually focus on statistical fairness, but there are broader justice implications relating to the data used to construct source databases, and how algorithms are incorporated into complex sociotechnical decision-making contexts. This article explores how data that inform child protection algorithms are produced and relates this production to both traditional notions of statistical fairness and broader justice concepts. Predictive tools have a number of challenging problems in the child protection context, as the data that predictive tools draw on do not represent child abuse incidence across the population and child abuse itself is difficult to define, making key decisions that become data variable and subjective. Algorithms using these data have distorted feedback loops and can contain inequalities and biases. The challenge to justice concepts is that individual and group rights to non-discrimination become threatened as the algorithm itself becomes skewed, leading to inaccurate risk predictions drawing on spurious correlations. The right to be treated as an individual is threatened when statistical risk is based on a group categorisation, and the rights of families to understand and participate in the decisions made about them is difficult when they have not consented to data linkage, and the function of the algorithm is obscured by its complexity. The use of uninterpretable algorithmic tools may create ‘moral crumple zones’, where practitioners are held responsible for decisions even when they are partially determined by an algorithm. Many of these criticisms can also be levelled at human decision makers in the child protection system, but the reification of these processes within algorithms render their articulation even more difficult, and can diminish other important relational and ethical aims of social work practice. View Full-Text
Keywords: child protection; predictive analytics; rights; social justice; algorithms; decision making child protection; predictive analytics; rights; social justice; algorithms; decision making
MDPI and ACS Style

Keddell, E. Algorithmic Justice in Child Protection: Statistical Fairness, Social Justice and the Implications for Practice. Soc. Sci. 2019, 8, 281.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop