Mitigating Algorithmic Bias in AI-Powered Toxicology: Frameworks for Explainable and Equitable Predictions in Human Health and Environmental Safety

Abstract

The rapid integration of artificial intelligence (AI) and machine learning into predictive toxicology has transformed chemical hazard identification, toxicity endpoint forecasting, and risk assessment for pharmaceuticals, environmental pollutants, and consumer products. While these tools promise faster, more ethical alternatives to traditional testing, they introduce significant risks of algorithmic bias arising from imbalanced datasets, historical data limitations, under-represented chemical classes or populations, and model design choices. Such biases can lead to inaccurate predictions, disproportionate errors in vulnerable subgroups (e.g., demographic or geographic disparities in human health outcomes), and unreliable environmental safety evaluations. This review synthesises sources of bias in AI-powered toxicology models, including data selection, feature engineering, and black-box opacity. It examines detection methods (e.g., fairness metrics, disparity audits) and mitigation frameworks, encompassing data-centric approaches (diverse inclusion, debiasing techniques, synthetic data generation), algorithmic interventions (adversarial training, regularisation), and holistic strategies aligned with emerging principles such as trustworthiness, reproducibility, explainability, applicability, and transparency. Special emphasis is placed on explainable AI (XAI) methods, such as SHAP, LIME, attention mechanisms, and Grad-CAM visualisations, that enhance interpretability, reveal mechanistic insights, and facilitate bias identification in toxicity predictions. Applications to human health (e.g., reducing inequities in adverse drug reaction forecasting) and environmental safety (e.g., equitable chemical prioritisation and ecosystem risk modelling) are highlighted, alongside regulatory considerations for acceptance of bias-resilient, transparent models. Challenges persist, including data heterogeneity, validation gaps, and ethical oversight needs. Future directions call for standardised bias auditing protocols, interdisciplinary collaboration, and policy frameworks to ensure AI-driven toxicology delivers fair, reliable, and human-relevant outcomes for safer public and environmental health.

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