Beyond Algorithmic Fairness: A Guide to Develop and Deploy Ethical AI-Enabled Decision-Support Tools

• URL: http://arxiv.org/abs/2409.11489v1
• Date: Tue, 17 Sep 2024 18:37:53 GMT
• Title: Beyond Algorithmic Fairness: A Guide to Develop and Deploy Ethical AI-Enabled Decision-Support Tools
• Authors: Rosemarie Santa Gonzalez, Ryan Piansky, Sue M Bae, Justin Biddle, Daniel Molzahn,
• Abstract summary: The integration of artificial intelligence (AI) and optimization hold substantial promise for improving the efficiency, reliability, and resilience of engineered systems. This paper identifies ethical considerations required when deploying algorithms at the intersection of AI and optimization. Rather than providing a prescriptive set of rules, this paper aims to foster reflection and awareness among researchers.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of artificial intelligence (AI) and optimization hold substantial promise for improving the efficiency, reliability, and resilience of engineered systems. Due to the networked nature of many engineered systems, ethically deploying methodologies at this intersection poses challenges that are distinct from other AI settings, thus motivating the development of ethical guidelines tailored to AI-enabled optimization. This paper highlights the need to go beyond fairness-driven algorithms to systematically address ethical decisions spanning the stages of modeling, data curation, results analysis, and implementation of optimization-based decision support tools. Accordingly, this paper identifies ethical considerations required when deploying algorithms at the intersection of AI and optimization via case studies in power systems as well as supply chain and logistics. Rather than providing a prescriptive set of rules, this paper aims to foster reflection and awareness among researchers and encourage consideration of ethical implications at every step of the decision-making process.

Related papers

• Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking [49.225507258297725]
  We introduce the fundamentals of Lyapunov optimization theory and analyze the limitations of both conventional methods and traditional AI-enabled approaches. We develop a Lyapunov-guided generative diffusion model-based reinforcement learning framework and validate its effectiveness through a UAV-based LAE networking case study.
  arXiv  Detail & Related papers  (2025-01-27T10:27:15Z)
• TOAST Framework: A Multidimensional Approach to Ethical and Sustainable AI Integration in Organizations [0.38073142980732994]
  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.
  arXiv  Detail & Related papers  (2025-01-07T05:13:39Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation. deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development [0.0]
  We propose a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance.
  arXiv  Detail & Related papers  (2024-11-05T18:38:30Z)
• AI-Driven Human-Autonomy Teaming in Tactical Operations: Proposed Framework, Challenges, and Future Directions [10.16399860867284]
  Artificial Intelligence (AI) techniques are transforming tactical operations by augmenting human decision-making capabilities. This paper explores AI-driven Human-Autonomy Teaming (HAT) as a transformative approach. We propose a comprehensive framework that addresses the key components of AI-driven HAT.
  arXiv  Detail & Related papers  (2024-10-28T15:05:16Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• A Vision for Operationalising Diversity and Inclusion in AI [5.4897262701261225]
  This study seeks to envision the operationalization of the ethical imperatives of diversity and inclusion (D&I) within AI ecosystems. A significant challenge in AI development is the effective operationalization of D&I principles. This paper proposes a vision of a framework for developing a tool utilizing persona-based simulation by Generative AI (GenAI)
  arXiv  Detail & Related papers  (2023-12-11T02:44:39Z)
• PerfDetectiveAI -- Performance Gap Analysis and Recommendation in Software Applications [0.0]
  PerfDetectiveAI, a conceptual framework for performance gap analysis and suggestion in software applications is introduced in this research. Modern machine learning (ML) and artificial intelligence (AI) techniques are used in PerfDetectiveAI to monitor performance measurements and identify areas of underperformance in software applications.
  arXiv  Detail & Related papers  (2023-06-11T02:53:04Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• 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)
• AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes [56.65379135797867]
  We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
  arXiv  Detail & Related papers  (2020-08-29T14:57:53Z)

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.