EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs
arXiv:2512.22240v5 Announce Type: replace-cross Abstract: Machine learning models are primarily judged by predictive performance, especially in applied genomics, where explanations are read as biological findings. In practice, reported gene panels are stabilised by averaging, ranking, or taking consensus over the many models a pipeline produces across cross-validation folds, tuning grids, and repeated runs. This raises an overlooked question: when two models achieve high accuracy, do they rely on the same internal logic, or reach the same outcome via different mechanisms? We introduce EvoXplai
The proliferation of high-accuracy machine learning models necessitates deeper understanding of their interpretability and underlying mechanisms, especially as AI applications become more critical and widespread.
Understanding mechanistic multiplicity is crucial for developing robust, reliable, and trustworthy AI systems, particularly in sensitive domains like genomics where explanations are treated as scientific findings.
This research introduces tools and a framework to systematically measure and evaluate how different AI models arrive at similar predictions, shifting focus from merely 'what' they predict to 'how' they predict.
- · AI explainability researchers
- · Applied genomics
- · Regulators of AI
- · Trustworthy AI developers
- · Black-box AI models
- · Developers solely focused on predictive performance
Increased demand for tools and methodologies that provide deeper insights into AI model decision-making processes.
Development of new model architectures and training paradigms explicitly designed for mechanistic interpretability.
Enhanced trust in AI systems could accelerate adoption in highly regulated and critical sectors, potentially leading to new ethical and governance frameworks.
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Read at arXiv cs.AI