Inherent Randomness

Inherent randomness in machine learning (ML) and related fields, such as quantum computing, is a significant area of research focusing on understanding and controlling the unpredictable elements within algorithms and data. Current investigations explore the impact of randomness on model reproducibility, fairness, and explainability, often employing techniques like hidden Markov models to analyze training dynamics and employing various model architectures, including recurrent neural networks and transformers. Addressing this inherent randomness is crucial for improving the reliability, trustworthiness, and generalizability of ML models, as well as for developing more robust and secure systems.