人工智能中伪科学的回归：机器学习和深度学习是否忘记了统计学和历史的教训？
The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?
AI总结：
本文指出机器学习和深度学习因忽视统计学中“相关性不代表因果性”的教训，产生类似伪科学的因果模型，仅靠减少训练数据偏差无法实现伦理AI，需重新设计核心模型、改进评估指标并保留人类监督。
摘要：
在当今世界，由机器学习驱动的人工智能程序无处不在，在从医疗诊断、银行信用评级到通过视频分析进行盗窃检测，甚至从面部图像预测政治倾向或性取向等广泛任务中都取得了看似卓越的性能。这些以深度学习为主的方法之所以表现出色，是因为它们具有非凡的能力，能够处理大量复杂数据，从不同层次的特征中提取复杂的相关性和关系。在本文中，我们认为这些机器学习方法的设计者和最终用户忘记了统计学中的一个基本教训：相关性并不意味着因果关系。大多数最先进的方法不仅忽视了这一关键原则，而且这样做往往会产生荒谬或有缺陷的因果模型，类似于社会占星术或面相学。因此，我们认为，当前仅通过减少训练数据中的偏差来使人工智能模型更具伦理的努力是不够的。通过例子，我们将证明，只有通过彻底重新思考其核心模型、改进质量评估指标和政策，以及在整个过程中保持人类监督，才能减轻这些方法带来的潜在危害。
英文摘要：
In today's world, AI programs powered by Machine Learning are ubiquitous, and have achieved seemingly exceptional performance across a broad range of tasks, from medical diagnosis and credit rating in banking, to theft detection via video analysis, and even predicting political or sexual orientation from facial images. These predominantly deep learning methods excel due to their extraordinary capacity to process vast amounts of complex data to extract complex correlations and relationship from different levels of features. In this paper, we contend that the designers and final users of these ML methods have forgotten a fundamental lesson from statistics: correlation does not imply causation. Not only do most state-of-the-art methods neglect this crucial principle, but by doing so they often produce nonsensical or flawed causal models, akin to social astrology or physiognomy. Consequently, we argue that current efforts to make AI models more ethical by merely reducing biases in the training data are insufficient. Through examples, we will demonstrate that the potential for harm posed by these methods can only be mitigated by a complete rethinking of their core models, improved quality assessment metrics and policies, and by maintaining humans oversight throughout the process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
