The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?
This paper highlights a foundational problem in AI for developers and users, warning that ignoring causal principles can lead to harmful and unethical outcomes, making it a critical critique rather than an incremental improvement.
The paper argues that current machine learning and deep learning methods often produce flawed causal models by neglecting the statistical principle that correlation does not imply causation, leading to potential harms such as nonsensical predictions in areas like medical diagnosis and facial analysis. It contends that addressing these issues requires rethinking core models, improving metrics and policies, and maintaining human oversight, rather than just reducing biases in training data.
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.