 Corresponding author: Martin Louis Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Scaling trustworthy AI: A framework for responsible system design Martin Louis * Independent Researcher, USA. Global Journal of Engineering and Technology Advances, 2023, 17(03), 089-098 Publication history: Received on 20 October 2023; revised on 28 November 2023; accepted on 30 November 2023 Article DOI: https://doi.org/10.30574/gjeta.2023.17.3.0232 Abstract AI technology is becoming more fluid and has impacted the society across a range of domains in a positive as well as in a negative manner. AI reliability is critical to society’s acceptance and preventing the risks connected to it hence the importance of the following measures. The purpose of this work is to synthesize a multifaceted approach to the further scaled implementation of trustworthy AI with a focus on responsible AI design. The framework is applied in theory as well as in two empirical cases, where the research combines theoretical and practical approaches. Main outcomes point to the importance of transparency management, accountability and ethical aspects in the effectiveness of AI applications. The discussed framework provides practical recommendations regarding how AI can be implemented responsibly at a large scale in an organization as an integrated system. The findings of this study must be viewed as significant for future development of AI technologies from both reliability and ethical perspectives with a view to ensuring the growth of public confidence in the application of reliable artificial intelligence technologies in the various sectors of our society. Keywords: Trustworthy AI; Responsible Design; Ethical Framework; AI Governance; Bias Mitigation; System Scalability 1. Introduction 1.1. Background to the Study The area of Artificial Intelligence (AI) has evolved from a restricted field to a large number of fields including the healthcare, financial sector, and transportation (Dwivedi et al., 2021). This rapid advancement has not only improved operational performance, but had also brought about issues that are ethical and social in nature. Issues of AI ethics such as bias, opacity and accountability, have grown louder as the AI systems become agents of their own and decision makers in their right (Dwivedi et al., 2021). Creating trust in AI is vital because it enables proper adoption of the technology, and reduce instances of risk that may negatively affect public faith and social welfare. The situation implies that as AI technologies advance further, the need to create suitable systems properly addresses the crucial and sensitive questions of ethical appropriateness and openness rises. To this end, this study responds to these challenges by designing a framework that incorporates ethical principles within the primary architecture and scaling of AI systems, allowing for their safe use across applications (Dwivedi et al., 2021). 1.2. Overview Trustworthy AI speaks of a number of principles describing how to build an ethical, transparent, and accountable artificial intelligent system. At the core of this idea, are the principles of fairness, accountability, transparency and privacy which work to counter bias and ensure for correct AI implementation (Thiebes et al., 2020). As Trustworthy AI has to be built within organizations, it is important that these principles are adopted in all stages of the AI life cycle,