# A Systematic Review of Responsible Artificial Intelligence Principles and Practice
## 5 Finding 3: Responsibility in AI  
Responsibility in the context of AI is a complex and multifaceted concept that requires a human-centered approach focused on well-being and alignment with societal values [22]. It involves the practical application of ethical, legal, economic, and cultural concerns to benefit society. To achieve this, responsible AI emphasizes the development and use of AI systems that benefit humans, society, and the environment while minimizing the risk of negative consequences [57]. The implementation of responsible AI depends on several fundamental principles that guide the design, development, and deployment of AI systems. These principles include transparency and explainability, fairness and algorithmic bias prevention, privacy and data protection, robustness and reliability, accountability, and social benefit [58]. Each principle addresses different aspects of responsible AI practice, together forming a comprehensive framework for ethical AI development. The ART principles--accountability, responsibility, and transparency--emerge as essential foundational elements [22; 23]. Here, responsibility relates to the role of people in relation to AI systems. This highlights that AI systems are socio-technical, and responsibility fundamentally lies with the people and organizations that create, develop, or use these systems [23]. As noted in [59], \"those who develop, create, and integrate AI into society should not be allowed to rescind their responsibility, simply because their creations act differently to how they were designed\". Responsible AI requires the systematic adoption of these principles for AI models to be of practical use. It involves designing ethical, transparent, and accountable solutions [22] that operate responsibly and meet stakeholder expectations and applicable regulations [42]. This necessitates Accountability-by-Design, where AI systems are designed to facilitate end-to-end answerability and auditability, establishing a continuous chain of human responsibility [60]. From an organizational perspective, responsible AI involves the integration of ethical and responsible use of AI into strategic implementation and planning processes [25]. It requires alignment of stakeholders' expectations and implementation of risk mitigation strategies. Ultimately, responsible AI aims to create systems that align with user expectations and prevalent societal laws, rules, and regulations [61], ensuring that AI technologies remain beneficial while minimizing potential harm. The terms \"Responsible AI\", \"Ethical AI\" and \"Trustworthy AI\" are often used interchangeably [62]; however, there are important differences between these terms. Ethical AI aims to align AI strictly in terms of human beings and their behavior, although this optimal and ideal alignment cannot be expected to be practically implemented [63]. Trustworthy AI refers to the dependability and subjectivity of what is perceived to be \"trust\", which can be misleading depending on the individuals or groups concerned [63]. Due to these ambiguities of the two prior terms, a new term was coined, \"Responsible AI\" as a more effective representation of the expectations of \"Ethical AI\" and \"Trustworthy AI\" with other broader concepts [63]. Governments, recognizing the growing importance of AI, are positioning themselves as leaders in shaping policies and regulations to govern its ethical and responsible use. This competition for leadership provides an impetus for countries to invest in developing robust frameworks that protect against the negative impacts of AI while promoting its beneficial applications. Similarly, organizations are capitalizing on the current focus on responsible AI practices. The heightened attention serves as an incentive for businesses to adopt ethical AI frameworks, not only to mitigate risks but also to align with societal expectations. Positive feedback from the public and stakeholders regarding responsible AI practices can improve a company's reputation and contribute to its competitive advantage.