Algorithms are increasingly influencing which families are scrutinized by child welfare services for neglect. Despite their purported benefits, these tools are far from flawless and have been shown to exhibit racial bias. This is not the first instance of algorithms designed to assist causing significant issues; from the divisive echo chambers of the 2016 elections to targeted advertising on social media, algorithms play a crucial role in shaping our perceptions and biases.
In a recent report by the Associated Press, as part of its ongoing series on the implications of algorithm-driven decisions, it was revealed that a predictive algorithm used by child welfare services in Allegheny County, Pennsylvania, disproportionately flags Black children for neglect investigations compared to their white peers. Research from Carnegie Mellon University, shared exclusively with AP, indicates that social workers often disagree with the algorithm’s assessments, with a staggering one-third of flagged cases being contested.
Determining the exact flaws within the algorithm is challenging. As highlighted by Rebecca Heiliweil from Vox, the complexity of algorithmic coding makes it difficult to pinpoint which elements contribute to biased outcomes. The Allegheny Family Screening Tool (AFST) lacks transparency regarding which factors are prioritized in its assessments, as it considers a range of vague criteria—from housing conditions to a child’s hygiene habits—potentially leading to arbitrary judgments based on data that can include anything from dental hygiene to bedtime routines.
Moreover, the AFST utilizes an extensive amount of personal data, including Medicaid records and criminal histories, which are inherently biased due to systemic issues within the institutions that provide this data. The programmers behind these algorithms, despite their intentions, are also subject to personal biases that can influence outcomes. This raises concerns among advocates for technological accountability, as unchecked algorithms can perpetuate and worsen existing social inequalities.
Public Citizen, a nonprofit consumer advocacy organization, has pointed out that algorithmic bias affects people of color across various aspects of life, from inflated insurance costs to social media censorship. For instance, predictive algorithms can cause communities of color to pay significantly more for car insurance than their white counterparts, even when accident rates are similar.
These so-called “black box algorithms” can be likened to the scenario in Fantasia, where Mickey Mouse’s attempt to automate a task with a broom spiraled out of control. Similarly, the unchecked application of algorithms like the AFST can lead to harmful, unintended consequences. Such tools are being implemented in various regions across the country, raising concerns that they may share the same biases and flaws, potentially harming families and children in need of support.
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Summary
Algorithms used in child welfare investigations are disproportionately flagging Black families for neglect, raising concerns about bias and accuracy. Research shows that social workers often disagree with the algorithm’s conclusions, highlighting the potential for harm when such tools are applied without oversight. The implications of algorithmic bias extend beyond child welfare into various areas of life, affecting communities of color significantly.
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