  The rapid development of Artificial Intelligence (AI) technology has enabled
the deployment of various systems based on it. However, many current AI systems
are found vulnerable to imperceptible attacks, biased against underrepresented
groups, lacking in user privacy protection. These shortcomings degrade user
experience and erode people's trust in all AI systems. In this review, we
provide AI practitioners with a comprehensive guide for building trustworthy AI
systems. We first introduce the theoretical framework of important aspects of
AI trustworthiness, including robustness, generalization, explainability,
transparency, reproducibility, fairness, privacy preservation, and
accountability. To unify currently available but fragmented approaches toward
trustworthy AI, we organize them in a systematic approach that considers the
entire lifecycle of AI systems, ranging from data acquisition to model
development, to system development and deployment, finally to continuous
monitoring and governance. In this framework, we offer concrete action items
for practitioners and societal stakeholders (e.g., researchers, engineers, and
regulators) to improve AI trustworthiness. Finally, we identify key
opportunities and challenges for the future development of trustworthy AI
systems, where we identify the need for a paradigm shift toward comprehensively
trustworthy AI systems.
