Human-centered AI (HCAI), as a design philosophy, advocates prioritizing humans in designing, developing, and deploying intelligent systems, aiming to maximize the benefits of AI technology to humans and avoid its potential adverse effects. While HCAI has gained momentum, the lack of guidance on methodology in its implementation makes its adoption challenging. After assessing the needs for a methodological framework for HCAI, this paper first proposes a comprehensive and interdisciplinary HCAI methodological framework integrated with seven components, including design goals, design principles, implementation approaches, design paradigms, interdisciplinary teams, methods, and processes. THe implications of the framework are also discussed. This paper also presents a "three-layer" approach to facilitate the implementation of the framework. We believe the proposed framework is systematic and executable, which can overcome the weaknesses in current frameworks and the challenges currently faced in implementing HCAI. Thus, the framework can help put it into action to develop, transfer, and implement HCAI in practice, eventually enabling the design, development, and deployment of HCAI-based intelligent systems.

Human-centered AI (HCAI) is a design philosophy that advocates prioritizing humans in designing, developing, and deploying intelligent systems, aiming to maximize the benefits of AI to humans and avoid potential adverse impacts. While HCAI continues to influence, the lack of guidance on methodology in practice makes its adoption challenging. This paper proposes a comprehensive HCAI framework based on our previous work with integrated components, including design goals, design principles, implementation approaches, interdisciplinary teams, HCAI methods, and HCAI processes. This paper also presents a "three-layer" approach to facilitate the implementation of the framework. We believe this systematic and executable framework can overcome the weaknesses in current HCAI frameworks and the challenges currently faced in practice, putting it into action to enable HCAI further.

This paper presents a memory-optimized metadata-based data structure for implementation of binary chromosome in Genetic Algorithm. In GA different types of genotypes are used depending on the problem domain. Among these, binary genotype is the most popular one for non-enumerated encoding owing to its representational and computational simplicity. This paper proposes a memory-optimized implementation approach of binary genotype. The approach improves the memory utilization as well as capacity of retaining alleles. Mathematical proof has been provided to establish the same.

This article talks about a solution stack that brings together and describes different types of artificial intelligence (AI) solutions which can be applied by companies towards introducing AI within their organization based on their specific implementation approach. With the recent hype in AI within the industry sectors, several companies are looking towards implementing AI based solutions from chatbots to machine learning based predictive engines. When a company thinks about implementing an AI solution they should first review their AI strategy, then confirm the implementation approach and finally decide on the solution stack. For the purpose of this article we will be dwelling on the implementation approach and solutioning parts only. Considering that the company has its AI strategy in place, I mean has answered questions like, what do you plan to accomplish by implementing AI within your organization and what business value will it deliver etc.