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Algorithmic tools are becoming embedded in pharmaceutical technology development, from formulation screening and excipient selection to process modelling, scale-up prediction, analytical method optimisation, and manufacturing control. These tools increasingly influence decisions that were historically made through human expert judgement, experimental iteration, and quality-system review. This shift creates a governance challenge because algorithmic recommendations may affect product quality, process robustness, patient safety, and regulatory compliance. Yet accountability for such decisions is often distributed ambiguously across data scientists, formulation scientists, process engineers, quality assurance units, regulatory teams, software vendors, and corporate management. The objective of this article is to construct an original governance framework for accountable algorithmic decision-making in pharmaceutical technology development. The framework is designed for settings where artificial intelligence, machine learning, and related computational decision-support tools influence formulation, process, analytical, or quality decisions. The proposed framework argues that accountability must be designed into algorithmic pharmaceutical development rather than retrofitted after model deployment. It defines algorithmic decision types, clarifies accountability triggers, and positions governance as part of the pharmaceutical quality system rather than as a separate digital compliance layer.

Pharmaceutical manufacturing is moving from automated equipment and digitally assisted control toward more autonomous systems capable of interpreting process data, adjusting operating conditions, and supporting quality decisions. This trajectory promises faster response, improved consistency, and more adaptive control across complex production environments. Yet autonomy also changes the nature of manufacturing responsibility because technical decisions increasingly occur inside algorithmic systems rather than through visible human judgement alone. The central problem addressed in this article is the trust deficit created by autonomous pharmaceutical manufacturing. When an algorithm modifies a critical process parameter, detects an anomaly, recommends batch continuation, or contributes to a quality disposition, regulators, operators, quality units, and patients require confidence that the decision remains safe, explainable, reversible, and accountable. Trust cannot be assumed simply because the system performs well during validation; it must be sustained over time as processes, materials, sensors, models, and organisational practices evolve. This article develops an original theory-driven framework for trustworthy autonomous pharmaceutical manufacturing. The framework is structured around three interdependent pillars: human oversight, model drift management, and quality accountability. These pillars are treated not as separate compliance add-ons but as mutually reinforcing design requirements for autonomous manufacturing systems operating in Good Manufacturing Practice environments. The article draws on a theoretical synthesis of peer-reviewed literature on pharmaceutical manufacturing automation, process analytical technology, machine learning, trust in automation, human–autonomy teaming, resilience engineering, socio-technical systems, and model drift. It reframes autonomous manufacturing as a socio-technical trust problem rather than a purely technical optimisation problem. Four tables map the theoretical foundations, oversight architectures, drift-management logic, and integrated Trustworthy System Framework. The proposed framework argues that trustworthiness in autonomous pharmaceutical manufacturing is not a property of an algorithm alone. It emerges from the designed relationship among people, models, process controls, quality systems, audit trails, and governance responsibilities. Autonomous manufacturing will become viable only when the system can remain technically reliable, humanly overseen, and institutionally accountable throughout its lifecycle.