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The rapid development of Artificial Intelligence (AI) technology has enabledthe deployment of various systems based on it. However, many current AI systemsare found vulnerable to imperceptible attacks, biased against underrepresentedgroups, lacking in user privacy protection. These shortcomings degrade userexperience and erode people's trust in all AI systems. In this review, weprovide AI practitioners with a comprehensive guide for building trustworthy AIsystems. We first introduce the theoretical framework of important aspects ofAI trustworthiness, including robustness, generalization, explainability,transparency, reproducibility, fairness, privacy preservation, andaccountability. To unify currently available but fragmented approaches towardtrustworthy AI, we organize them in a systematic approach that considers theentire lifecycle of AI systems, ranging from data acquisition to modeldevelopment, to system development and deployment, finally to continuousmonitoring and governance. In this framework, we offer concrete action itemsfor practitioners and societal stakeholders (e.g., researchers, engineers, andregulators) to improve AI trustworthiness. Finally, we identify keyopportunities and challenges for the future development of trustworthy AIsystems, where we identify the need for a paradigm shift toward comprehensivelytrustworthy AI systems.

Trustworthy AI: From Principles to Practices