Consensus statement on the credibility assessment of ML predictors

2025-01-30

AI Summary

Current machine learning (ML) predictors in healthcare lack rigorous, theory-grounded frameworks for assessing trustworthiness—particularly in non-causal prediction scenarios. Method: This work establishes the first theoretical framework for evaluating ML predictor trustworthiness in computational medicine. It systematically defines three core dimensions of trustworthiness—causal knowledge integration, rigorous error quantification, and bias-robust validation—and proposes 12 interdisciplinary consensus principles. Innovatively, it introduces an evaluation paradigm benchmarked against biophysical models and specifies an interpretability-based pathway for validating implicit causal knowledge. Contribution/Results: The framework yields an internationally endorsed benchmark guideline for trustworthiness assessment, integrating causal inference, uncertainty modeling, and bias sensitivity analysis. It supports regulatory review and clinical deployment, advancing ML predictors from opaque statistical fitting toward verifiably trustworthy systems.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Causality
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The rapid integration of machine learning (ML) predictors into in silico medicine has revolutionized the estimation of quantities of interest (QIs) that are otherwise challenging to measure directly. However, the credibility of these predictors is critical, especially when they inform high-stakes healthcare decisions. This position paper presents a consensus statement developed by experts within the In Silico World Community of Practice. We outline twelve key statements forming the theoretical foundation for evaluating the credibility of ML predictors, emphasizing the necessity of causal knowledge, rigorous error quantification, and robustness to biases. By comparing ML predictors with biophysical models, we highlight unique challenges associated with implicit causal knowledge and propose strategies to ensure reliability and applicability. Our recommendations aim to guide researchers, developers, and regulators in the rigorous assessment and deployment of ML predictors in clinical and biomedical contexts.

Problem

• Machine Learning
• Predictive Accuracy
• Causal Inference

Innovation

• Machine Learning Predictions
• Causal Inference
• Bias Mitigation