预测式AI为什么一败涂地？

Group 1 - The core argument of the articles revolves around the challenges and implications of predictive AI systems, particularly in decision-making processes across various sectors, including education, healthcare, and criminal justice [1][2][4][10]. - Predictive AI tools like EAB Navigate are designed to automate decision-making by analyzing historical data to predict future outcomes, but they often lack transparency and can perpetuate biases [2][9][10]. - The use of predictive AI in education, such as identifying at-risk students, raises ethical concerns about the potential for misuse and the impact on marginalized groups [1][8][29]. Group 2 - Predictive AI systems are increasingly used in critical areas like healthcare and criminal justice, where they can significantly affect individuals' lives, yet they often rely on flawed data and assumptions [6][12][31]. - The deployment of predictive AI can lead to unintended consequences, such as reinforcing existing inequalities and biases, particularly against disadvantaged populations [28][30][31]. - The reliance on historical data for training predictive models can result in a lack of accuracy when applied to different populations or contexts, highlighting the need for careful consideration of the data used [24][25][27]. Group 3 - The articles emphasize the importance of understanding the limitations of predictive AI, including the potential for over-automation and the lack of accountability in decision-making processes [20][22][23]. - There is a growing concern about the ethical implications of using predictive AI, particularly regarding privacy, transparency, and the potential for discrimination [21][28][30]. - The narrative suggests that while predictive AI holds promise for improving efficiency, it also poses significant risks that must be addressed through better data practices and ethical guidelines [15][19][35].