Decoding the Black Box: Integrating Moral Imagination with Technical AI Governance
- URL: http://arxiv.org/abs/2503.06411v1
- Date: Sun, 09 Mar 2025 03:11:32 GMT
- Title: Decoding the Black Box: Integrating Moral Imagination with Technical AI Governance
- Authors: Krti Tallam,
- Abstract summary: We develop a comprehensive framework designed to regulate AI technologies deployed in high-stakes domains such as defense, finance, healthcare, and education.<n>Our approach combines rigorous technical analysis, quantitative risk assessment, and normative evaluation to expose systemic vulnerabilities.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper examines the intricate interplay among AI safety, security, and governance by integrating technical systems engineering with principles of moral imagination and ethical philosophy. Drawing on foundational insights from Weapons of Math Destruction and Thinking in Systems alongside contemporary debates in AI ethics, we develop a comprehensive multi-dimensional framework designed to regulate AI technologies deployed in high-stakes domains such as defense, finance, healthcare, and education. Our approach combines rigorous technical analysis, quantitative risk assessment, and normative evaluation to expose systemic vulnerabilities inherent in opaque, black-box models. Detailed case studies, including analyses of Microsoft Tay (2016) and the UK A-Level Grading Algorithm (2020), demonstrate how security lapses, bias amplification, and lack of accountability can precipitate cascading failures that undermine public trust. We conclude by outlining targeted strategies for enhancing AI resilience through adaptive regulatory mechanisms, robust security protocols, and interdisciplinary oversight, thereby advancing the state of the art in ethical and technical AI governance.
Related papers
- Integrating Generative AI in Cybersecurity Education: Case Study Insights on Pedagogical Strategies, Critical Thinking, and Responsible AI Use [0.0]
This study presents a structured framework for integrating GenAI tools into cybersecurity education.<n>The implementation strategy followed a two-stage approach, embedding GenAI within tutorial exercises and assessment tasks.<n>Findings indicate that AI-assisted learning significantly enhanced students' ability to evaluate security policies, refine risk assessments, and bridge theoretical knowledge with practical application.
arXiv Detail & Related papers (2025-02-21T10:14:07Z) - Securing the AI Frontier: Urgent Ethical and Regulatory Imperatives for AI-Driven Cybersecurity [0.0]
This paper critically examines the evolving ethical and regulatory challenges posed by the integration of artificial intelligence in cybersecurity.<n>We trace the historical development of AI regulation, highlighting major milestones from theoretical discussions in the 1940s to the implementation of recent global frameworks such as the European Union AI Act.<n>Ethical concerns such as bias, transparency, accountability, privacy, and human oversight are explored in depth, along with their implications for AI-driven cybersecurity systems.
arXiv Detail & Related papers (2025-01-15T18:17:37Z) - 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.<n>It focuses on reliability, accountability, technical advancement, adaptability, and socio-technical harmony.<n>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) - Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act)
Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence.
As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
arXiv Detail & Related papers (2024-10-10T17:38:38Z) - Open Problems in Technical AI Governance [93.89102632003996]
Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI.
This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
arXiv Detail & Related papers (2024-07-20T21:13:56Z) - Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI.
The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees.
We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
arXiv Detail & Related papers (2024-05-10T17:38:32Z) - 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) - Managing extreme AI risks amid rapid progress [171.05448842016125]
We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems.
There is a lack of consensus about how exactly such risks arise, and how to manage them.
Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
arXiv Detail & Related papers (2023-10-26T17:59:06Z) - 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.