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In this podcast, the hosts discuss the problems arising from the use of machine learning algorithms in welfare systems across Europe. They explore 'suspicion machines' that generate suspicion of certain groups in need of welfare benefits. The podcast also delves into a case study on a model deployed by Rotterdam to detect fraud, including the limitations of the dataset and the impact of being flagged for investigation. The speakers discuss the process of building a predictive model for Rotterdam and the challenges and questions surrounding systems with probabilistic assessment.
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Quick takeaways
- The podcast highlights the limitations and flaws of machine learning systems in real-world settings, emphasizing the need to learn from these flaws to create more trustworthy AI systems.
- The discussion focuses on suspicion machines in European welfare systems, revealing how flawed data and features contribute to discriminatory outcomes and raise questions about fairness and ethics.
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Understanding the Flaws in Deployed Machine Learning Systems
The podcast explores the limitations and flaws in machine learning systems that are currently deployed in real-world settings. The guests discuss how the focus on AI dangers overlooks the existing problems with deployed machine learning systems. They highlight the need to learn from these flaws to create better and more trustworthy AI systems in the future. The discussion centers around suspicion machines, specifically the use of predictive risk assessments in European welfare systems. These systems assign risk scores to individuals and rank them by their alleged risk of welfare fraud, leading to investigations and potential benefit cuts. The guests emphasize that suspicion machines often generate suspicion of different groups trying to receive welfare benefits, and they highlight the consequences and punitiveness faced by individuals flagged by these systems.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode