Artificial intelligence is being innovatively applied to protect vulnerable populations, such as children in foster care and adults in nursing homes, by providing real-time alerts for potential abuse. Tools like natural language processing and predictive modeling help identify individuals at risk of harm. While these technologies promise to make interventions more efficient and timely, concerns arise from their reliance on historical data, which may replicate existing social biases. The intersection of AI and social welfare raises critical questions about whether new technologies can enact meaningful change or merely reinforce past injustices.
As a social worker with 15 years of experience and now developing iCare, I confront whether AI can truly safeguard vulnerable populations or if it simply replicates existing biases.
Many AI tools trained on historical data risk perpetuating systemic discrimination and bias, potentially failing to deliver the intended protection for vulnerable individuals.
Thoughtfully implemented, AI can enhance safety, enabling social workers to prioritize high-risk cases; yet poorly designed systems may harm those they aim to protect.
Predictive modeling and natural language processing in AI aim to identify risks to vulnerable individuals, but flaws in these systems mirror historical inequalities that must be addressed.
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