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An algorithm is increasingly influencing the decisions made by child welfare services regarding which families to investigate for neglect. Despite its intended purpose, this screening tool has demonstrated significant flaws, including racial bias. This issue is not new; algorithms designed to assist often lead to unintended negative consequences. They have contributed to various societal issues, from the echo chambers of the 2016 elections to the targeted advertising on social media, shaping the information we consume and the biases we hold.
In a recent report by the Associated Press, part of their series “Tracked,” which examines the impact of algorithms on daily life, it was revealed that a predictive algorithm used in Allegheny County, Pennsylvania, disproportionately flags Black children for mandatory neglect investigations compared to white children. Research from Carnegie Mellon University indicated that social workers disagreed with the algorithm’s risk assessments in about one-third of the cases. Essentially, if the algorithm were graded, it would receive a D+.
The specifics of what makes this algorithm problematic are complex. As noted by journalist Rebecca Heilweil, identifying which aspects of an algorithm’s coding lead to bias is challenging. The Allegheny Family Screening Tool (AFST) lacks transparency, making it difficult for the public to understand the factors that contribute to its assessments. The algorithm considers a range of vague issues, such as inadequate housing and poor hygiene, which can be influenced by subjective interpretations of data.
Moreover, the AFST utilizes a wide array of personal data collected from birth, including Medicaid records and criminal histories—data sources that are already biased against marginalized groups. These biases are not mitigated by an unbiased computer; they reflect the biases of the programmers who create the algorithms. This raises concerns among advocates for tech accountability, as unchecked algorithms can perpetuate systemic inequalities.
Organizations like Public Citizen have highlighted that biased algorithms have tangible effects on people of color across various sectors. For instance, communities of color often pay significantly more for car insurance compared to white communities with similar accident rates due to predictive algorithms. Additionally, social media platforms have faced backlash from Black creators whose content is frequently removed due to algorithmic errors.
The situation is reminiscent of the scene in Fantasia where Mickey Mouse, as the sorcerer’s apprentice, uses a broom to perform his tasks more efficiently, leading to chaos when he loses control. Similarly, unchecked algorithms can replicate harmful biases at an alarming rate. The AFST is not an isolated case; similar algorithms are being deployed in other regions, potentially causing widespread harm.
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Summary
The use of algorithms in child welfare investigations has raised significant concerns, particularly regarding racial bias and the accuracy of assessments. Recent findings show that an algorithm in Allegheny County disproportionately flags Black families for neglect investigations, with social workers frequently disagreeing with its evaluations. This highlights the broader issue of algorithmic bias and its potential to exacerbate existing societal inequalities.
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