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Discover the tangible problems emerging from the use of machine learning algorithms in Europe, specifically 'suspicion machines' that assign scores to welfare program participants. Justin and Gabriel share insights from their investigation into one of these models, discussing limitations, biased data, and the importance of transparency and ethical considerations.
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- Machine learning models in European welfare systems assign risk scores to welfare recipients, leading to potential unfair treatment and targeting of certain groups.
- Data journalists investigating the use of machine learning models in welfare systems found flaws in their construction, including biased outcomes and disparate impacts on certain groups.
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The Problems with Deployed Machine Learning Systems
There is a need to address the real-world problems with deployed machine learning systems despite the focus on the dangers and risks of AI. Machine learning models in European welfare systems, for example, assign risk scores to welfare recipients, leading to investigations and potential benefit stoppages for those with high scores. Such systems generate suspicion and may unfairly target certain groups. Examples of the negative consequences of these systems include wrongful accusations and punitive investigations. The scale of welfare fraud is hard to quantify accurately, but estimates vary significantly, with consultancies often hyping up the estimates. The overall effectiveness and fairness of these systems are questionable, with evidence of biased outcomes and harsh treatment for those investigated.
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