TOAST Framework: A Multidimensional Approach to Ethical and Sustainable AI Integration in Organizations

• URL: http://arxiv.org/abs/2502.00011v1
• Date: Tue, 07 Jan 2025 05:13:39 GMT
• Title: TOAST Framework: A Multidimensional Approach to Ethical and Sustainable AI Integration in Organizations
• Authors: Dian Tjondronegoro,
• Abstract summary: This paper introduces the Trustworthy, Optimized, Adaptable, and Socio-Technologically harmonious (TOAST) framework. It focuses 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.
• Score: 0.38073142980732994
• License:
• Abstract: 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.

Related papers

• Safety is Essential for Responsible Open-Ended Systems [47.172735322186]
  Open-Endedness is the ability of AI systems to continuously and autonomously generate novel and diverse artifacts or solutions. This position paper argues that the inherently dynamic and self-propagating nature of Open-Ended AI introduces significant, underexplored risks.
  arXiv  Detail & Related papers  (2025-02-06T21:32:07Z)
• Transforming the Hybrid Cloud for Emerging AI Workloads [81.15269563290326]
  This white paper envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads. The proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness. This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms.
  arXiv  Detail & Related papers  (2024-11-20T11:57:43Z)
• Reliability, Resilience and Human Factors Engineering for Trustworthy AI Systems [6.120331132009475]
  We offer a framework that integrates established reliability and resilience engineering principles into AI systems. We propose an integrate framework to manage AI system performance, and prevent or efficiently recover from failures. We apply our framework to a real-world AI system, using system status data from platforms such as openAI, to demonstrate its practical applicability.
  arXiv  Detail & Related papers  (2024-11-13T19:16:44Z)
• Ethical and Scalable Automation: A Governance and Compliance Framework for Business Applications [0.0]
  This paper introduces a framework ensuring that AI must be ethical, controllable, viable, and desirable. Different case studies validate this framework by integrating AI in both academic and practical environments.
  arXiv  Detail & Related papers  (2024-09-25T12:39:28Z)
• Navigating the sociotechnical labyrinth: Dynamic certification for responsible embodied AI [19.959138971887395]
  We argue that sociotechnical requirements shape the governance of artificially intelligent (AI) systems. Our proposed transdisciplinary approach is designed to ensure the safe, ethical, and practical deployment of AI systems.
  arXiv  Detail & Related papers  (2024-08-16T08:35:26Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results. However, the deployment of these agents in physical environments presents significant safety challenges. This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Quantifying AI Vulnerabilities: A Synthesis of Complexity, Dynamical Systems, and Game Theory [0.0]
  We propose a novel approach that introduces three metrics: System Complexity Index (SCI), Lyapunov Exponent for AI Stability (LEAIS), and Nash Equilibrium Robustness (NER) SCI quantifies the inherent complexity of an AI system, LEAIS captures its stability and sensitivity to perturbations, and NER evaluates its strategic robustness against adversarial manipulation.
  arXiv  Detail & Related papers  (2024-04-07T07:05:59Z)
• Developing and Deploying Industry Standards for Artificial Intelligence in Education (AIED): Challenges, Strategies, and Future Directions [22.65961106637345]
  The adoption of Artificial Intelligence in Education (AIED) holds the promise of revolutionizing educational practices. The lack of standardized practices in the development and deployment of AIED solutions has led to fragmented ecosystems. This article aims to address the critical need to develop and implement industry standards in AIED.
  arXiv  Detail & Related papers  (2024-03-13T22:38:08Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• Trustworthy AI: From Principles to Practices [44.67324097900778]
  Many current AI systems were found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection, etc. In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems. To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems.
  arXiv  Detail & Related papers  (2021-10-04T03:20:39Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.