Industry 5.0 highlights human-centricity, sustainability, and resilience. This article presents a novel Trust by Design framework applicable to collaborative intelligence systems within Industry 5.0, addressing the need for collaborative systems to be reliable by design, incorporating ethical principles such as transparency, accountability, fairness, and privacy throughout the entire system lifecycle. The framework is grounded in select ethical philosophies applied to practical design requirements for human-AI collaboration, identifying key ethical challenges that threaten to damage trust and restrict the adoption of collaborative systems. The authors employ a qualitative, literature-driven method, conceptual modeling, and scenario-based case study analysis, synthesizing best practices and ethical policies from the EU AI Act, GDPR, and more. Trust by Design suggests a structured set of principles and implementation measures to embed ethics into every phase of the system’s lifecycle. The applicability and suitability of the framework are demonstrated through representative real-world application scenarios across industries. The results indicate that trust in collaborative intelligence systems is not static but dynamic, context-dependent, and controlled by transparency, fairness, and user experience. The framework includes instruments and methods to measure ethical performance, including trust metrics, override rates, fairness indicators, and incident tracking.

The growing volume of scientific output and the pace of AI advancement create a dual challenge: traditional systematic reviews take twelve to eighteen months to complete, risking obsolescence before publication, while the technology needed to accelerate them is itself advancing faster than it can be reviewed. This work addresses both sides of that challenge simultaneously—presenting an agentic systematic literature review of AI-assisted reviewing, conducted using the same agentic pipeline it evaluates, across 122 primary studies published between 2022 and 2026. LLM performance proves to be sharply stage-specific: screening and structured extraction are tractable under human supervision, quality appraisal remains near-chance, and no published pipeline meets the bar for unsupervised end-to-end deployment. The review proposes a seven-layer reference architecture and identifies three prerequisite investments for production readiness: a shared benchmark, validated risk-of-bias performance, and a mandatory AI reporting standard.