Making ML Research Count – Avoiding the Pitfalls of Spurious Findings

The growth of machine-learning conference submissions and rapid uptake of large language models have accelerated research, while leaving open the question of whether findings have become more reliable. A favourable random seed or contamination of a test set can be mistaken for a scientific advance. This lecture examines the resulting gap in trust, analyses common research practices that produce fragile conclusions, and discusses safeguards needed for machine-learning studies to make a durable scientific contribution.