TOAST Framework: A Multidimensional Approach to Ethical and Sustainable AI Integration in Organizations
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Abstract (arXiv)
Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various sectors, from healthcare to finance, education, and beyond. However, successfully implementing AI systems remains a complex challenge, requiring a comprehensive and methodologically sound framework. This paper contributes to this challenge by introducing the Trustworthy, Optimized, Adaptable, and Socio-Technologically harmonious (TOAST) framework. It draws on insights from various disciplines to align technical strategy with ethical values, societal responsibilities, and innovation aspirations. The TOAST framework is a novel approach designed to guide the implementation of AI systems, focusing on reliability, accountability, technical advancement, adaptability, and socio-technical harmony. By grounding the TOAST framework in healthcare case studies, this paper provides a robust evaluation of its practicality and theoretical soundness in addressing operational, ethical, and regulatory challenges in high-stakes environments, demonstrating how adaptable AI systems can enhance institutional efficiency, mitigate risks like bias and data privacy, and offer a replicable model for other sectors requiring ethically aligned and efficient AI integration.
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References · 9
- 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 384
- 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 183
- 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 167
- 2019Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AIcited by 63
- 2020Trustworthy AIcited by 62
- 2022AI for Next Generation Computing: Emerging Trends and Future Directionscited by 13
- 2020Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Contextcited by 7
- 2021Socio-Technical Grounded Theory for Software Engineeringcited by 7
- 2020A Hybrid Approach Combining Control Theory and AI for Engineering Self-Adaptive Systemscited by 4
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