Safe and Trustworthy AI Framework (STAIF): Addressing Uncertainty in AI-Based Models for Real-World Deployment

While artificial intelligence (AI) has great promise for revolutionizing many facets of our lives, questions about its ethical and long-term use still need to be answered. Recognizing the widespread impact of AI, governments have initiated legislation, with the European Union's AI Act and the United States' AI Act as pioneering efforts. AI's connection to sustainability factors, including trustworthiness, reliability, privacy and security, efficiency, and ethics, necessitates careful consideration. This paper proposes a framework for integrating safe and trustworthy AI practices into AI development and deployment, aimed at promoting reliability, efficiency, and sustainability. Extending the Software Failure Modes and Effects Analysis (SFMEA), the framework proactively identifies and mitigates potential risks based on system criticality, enhancing reliability and trust. Additionally, the paper discusses implementation strategies for building trustworthy AI systems, incorporating sustainability goals, and presents a case study using this framework. The conclusion highlights the framework's potential to guide the development of responsible AI for a sustainable future, acknowledging ongoing challenges and future research directions.