Responsible AI Implementation: A Human-centered Framework for Accelerating the Innovation Process

There is still a significant gap between expectations and the successful adoption of AI to innovate and improve businesses. Due to the emergence of deep learning, AI adoption is more complex as it often incorporates big data and the internet of things (IoT), affecting data privacy. Existing frameworks have identified the need to focus on human-centered design, combining technical and business/organizational perspectives. However, trust remains a critical issue that needs to be designed from the beginning. The proposed framework is the first to expand from the human-centered design approach, emphasizing and maintaining the trust that underpins the whole process. This paper proposes a new theoretical framework for responsible artificial intelligence (AI) implementation. The proposed framework emphasizes a synergistic business-technology approach for the agile co-creation process. The aim is to streamline the adoption process of AI to innovate and improve business by involving all stakeholders throughout the project so that the AI technology is designed, developed, and deployed in conjunction with people and not in isolation. The framework presents a fresh viewpoint on responsible AI implementation based on analytical literature review, conceptual framework design, and practitioners' mediating expertise. The framework emphasizes establishing and maintaining trust throughout the human-centered design and agile development of AI. This human-centered approach is aligned with and enabled by the "privacy-by-design" principle. The creators of the technology and the end-users are working together to tailor the AI solution specifically for the business requirements and human characteristics. An illustrative case study on adopting AI for assisting planning in a hospital will demonstrate that the proposed framework applies to real-life applications. Keywords Technology management, Artificial intelligence, Responsible AI, Business adoption framework, Human-centered design, Agile methodology 1. Introduction Despite the rapid advancement and growth of global investment in Artificial Intelligence (AI) technologies, a survey with more than 3000 business executives has recently revealed that only a fifth have incorporated AI in their processes (Ransbotham et al. 2017). The lack of adoption shows a significant gap between ambition and execution for implementing AI to innovate and improve businesses. Meanwhile, due to the rapid growth of AI-enabled automation, people become more worried about losing jobs and control over their data.