000			02965nab a22002777a 4500
999			_c7383 _d7383
005			20250625151601.0
008			211201s2019 -n|| |||| 00| 0 eng d
040			_aAFVC
100			_aKeddell, Emily _94218
245			_aAlgorithmic justice in child protection : _bstatistical fairness, social justice and the implications for practice _cEmily Keddell
260			_bMDPI, _c2019
500			_aSocial Sciences, 2019, 8, 281
520			_aAlgorithmic 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. (Author's abstract). Record #7383
650			_aCHILD PROTECTION _9118
650			_aCHILDREN'S RIGHTS _9135
650		0	_94928 _aPREDICTIVE RISK MODELLING
650		4	_aRISK ASSESSMENT _9504
650			_aSOCIAL JUSTICE _910466
650			_aSOCIAL POLICY _9551
650		4	_aSOCIAL SERVICES _9555
651		4	_aNEW ZEALAND _92588
773	0		_tSocial Sciences, 2019, 8, 281
830			_aSocial Sciences _96421
856			_uhttps://doi.org/10.3390/socsci8100281 _zDOI: 10.3390/socsci8100281 (Open access)
942			_2ddc _cARTICLE