Assessing Company Contributions to Societal Resilience: Extending the Societal Capacity Assessment Framework to Agentic AI

• URL: http://arxiv.org/abs/2608.27238v1
• Date: Thu, 27 Aug 2026 15:21:41 GMT
• Title: Assessing Company Contributions to Societal Resilience: Extending the Societal Capacity Assessment Framework to Agentic AI
• Abstract summary: This paper adapts the indicator-based Societal Capacity Assessment Framework to measure how a company's deployment decisions contribute to societal resilience.<n>Our procedure has two steps: a conceptual step in which we design a suite of indicators that define what SCAF's vulnerability, coping, and adaptive capacities mean when assessing a company's AI deployment decisions; and a measurement step in which we apply this framework in a structured assessment of public-facing Microsoft documents.
• Score: 40.57385775451146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Companies that deploy AI agents and make them available to others are creating the sociotechnical circumstances under which this technology integrates into existing social and economic structures. AI-deploying companies are institutional actors that actively shape society's capacity to withstand and govern the consequences of agentic AI. In view of these societal impacts, companies can build societal resilience by designing and promoting safer implementations of AI agents. To operationalize this goal, this paper adapts the indicator-based Societal Capacity Assessment Framework (SCAF) to measure how a company's deployment decisions contribute to societal resilience, inverting its original measurement of societal resilience as a backdrop for deployment decisions (Gandhi et al., 2025). Our procedure has two steps: a conceptual step in which we design a suite of indicators that define what SCAF's vulnerability, coping, and adaptive capacities mean when assessing a company's agentic AI deployment decisions; and a measurement step in which we apply this framework in a structured assessment of public-facing Microsoft documents.

Related papers

• Beyond Model Readiness: Institutional Readiness for AI Deployment in Public Systems [0.0]
  Many public-sector artificial intelligence systems fail not at the point of model development, but at the point of deployment.<n>Existing responsible AI and model evaluation frameworks are valuable, but they primarily assess models, datasets, and developer-side processes.<n>We introduce Institutional Alignment Readiness (IAR), a five-dimensional framework for assessing deployment readiness in public systems.
  arXiv  Detail & Related papers  (2026-05-17T00:11:09Z)
• Beyond Benchmark Islands: Toward Representative Trustworthiness Evaluation for Agentic AI [45.21562889170875]
  We propose a systematic evaluation paradigm that characterizes agent trustworthiness over a scenario manifold.<n>The framework integrates four complementary components: static cognitive and policy analysis, (ii) interactive sandbox simulation, (iii) social-ethical alignment assessment, and (iv) a distribution-aware representative sampling engine.
  arXiv  Detail & Related papers  (2026-03-16T08:51:33Z)
• Adaptation of Agentic AI [162.63072848575695]
  We unify the rapidly expanding research landscape into a systematic framework that spans both agent adaptations and tool adaptations.<n>We demonstrate that this framework helps clarify the design space of adaptation strategies in agentic AI.<n>We then review the representative approaches in each category, analyze their strengths and limitations, and highlight key open challenges and future opportunities.
  arXiv  Detail & Related papers  (2025-12-18T08:38:51Z)
• QOC DAO - Stepwise Development Towards an AI Driven Decentralized Autonomous Organization [0.0]
  This paper introduces a structured approach to improving decision making in Decentralized Autonomous Organizations (DAO)<n>We outline a stepwise governance framework that evolves from human led evaluations to fully autonomous, AI-driven processes.
  arXiv  Detail & Related papers  (2025-11-10T12:55:23Z)
• Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents [63.03252293761656]
  This paper systematically reviews the technologies, applications, and evaluation methods of industry agents based on large language models (LLMs)<n>We examine the three key technological pillars that support the advancement of agent capabilities: Memory, Planning, and Tool Use.<n>We provide an overview of the application of industry agents in real-world domains such as digital engineering, scientific discovery, embodied intelligence, collaborative business execution, and complex system simulation.
  arXiv  Detail & Related papers  (2025-10-20T12:46:55Z)
• Agentic Services Computing [51.50424046053763]
  We propose Agentic Services Computing, a paradigm that reimagines services as autonomous, adaptive, and collaborative agents.<n>We organize ASC around a four-phase lifecycle: Design, Deployment, Operation, and Evolution.
  arXiv  Detail & Related papers  (2025-09-29T07:29:18Z)
• Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management [0.14963505712040906]
  Societal Capacity Assessment Framework (SCAF) is an indicators-based approach to measuring a society's vulnerability, coping capacity, and adaptive capacity in response to AI-related risks.<n>SCAF adapts established resilience analysis methodologies to AI, enabling organisations to ground risk management in insights about country-level deployment conditions.
  arXiv  Detail & Related papers  (2025-09-26T02:55:53Z)
• From Firms to Computation: AI Governance and the Evolution of Institutions [0.0]
  This article synthesizes three frameworks: multi-level selection theory, Aoki's view of firms as computational processes, and Ostrom's design principles for robust institutions.<n>We develop a framework where selection operates concurrently across organizational levels, firms implement distributed inference via game-theoretic architectures, and Ostrom-style rules evolve as alignment mechanisms that address AI-related risks.<n>We conclude by proposing a set of design principles that operationalize alignment between humans and AI across institutional layers, enabling scalable, adaptive, and inclusive governance of agential AI systems.
  arXiv  Detail & Related papers  (2025-07-18T02:52:58Z)
• Assistive AI for Augmenting Human Decision-making [3.379906135388703]
  The paper shows how AI can assist in the complex process of decision-making while maintaining human oversight. Central to our framework are the principles of privacy, accountability, and credibility.
  arXiv  Detail & Related papers  (2024-10-18T10:16:07Z)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

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.