Towards a Capability Assessment Model for the Comprehension and Adoption of AI in Organisations

• URL: http://arxiv.org/abs/2305.15922v1
• Date: Thu, 25 May 2023 10:43:54 GMT
• Title: Towards a Capability Assessment Model for the Comprehension and Adoption of AI in Organisations
• Authors: Butler, Tom, Espinoza-Lim\'on, Angelina, and Sepp\"al\"a, Selja
• Abstract summary: This article presents a 5-level AI Capability Assessment Model (AI-CAM) and a related AI Capabilities Matrix (AI-CM) The AI-CAM covers the core capability dimensions (business, data, technology, organisation, AI skills, risks, and ethical considerations) required at the five capability maturity levels to achieve optimal use of AI in organisations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The comprehension and adoption of Artificial Intelligence (AI) are beset with practical and ethical problems. This article presents a 5-level AI Capability Assessment Model (AI-CAM) and a related AI Capabilities Matrix (AI-CM) to assist practitioners in AI comprehension and adoption. These practical tools were developed with business executives, technologists, and other organisational stakeholders in mind. They are founded on a comprehensive conception of AI compared to those in other AI adoption models and are also open-source artefacts. Thus, the AI-CAM and AI-CM present an accessible resource to help inform organisational decision-makers on the capability requirements for (1) AI-based data analytics use cases based on machine learning technologies; (2) Knowledge representation to engineer and represent data, information and knowledge using semantic technologies; and (3) AI-based solutions that seek to emulate human reasoning and decision-making. The AI-CAM covers the core capability dimensions (business, data, technology, organisation, AI skills, risks, and ethical considerations) required at the five capability maturity levels to achieve optimal use of AI in organisations.

Related papers

• 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)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• The Ethics of Advanced AI Assistants [53.89899371095332]
  This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user. We consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants.
  arXiv  Detail & Related papers  (2024-04-24T23:18:46Z)
• 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)
• End-User Development for Artificial Intelligence: A Systematic Literature Review [2.347942013388615]
  End-User Development (EUD) can allow people to create, customize, or adapt AI-based systems to their own needs. This paper presents a literature review that aims to shed the light on the current landscape of EUD for AI systems.
  arXiv  Detail & Related papers  (2023-04-14T09:57:36Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• 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 Governance for Businesses [2.072259480917207]
  It aims at leveraging AI through effective use of data and minimization of AI-related cost and risk. This work views AI products as systems, where key functionality is delivered by machine learning (ML) models leveraging (training) data. Our framework decomposes AI governance into governance of data, (ML) models and (AI) systems along four dimensions.
  arXiv  Detail & Related papers  (2020-11-20T22:31:37Z)
• Enterprise AI Canvas -- Integrating Artificial Intelligence into Business [0.0]
  The Enterprise AI canvas is designed to bring Data Scientist and business expert together to discuss and define all relevant aspects. It consists of two parts where part one focuses on the business view and organizational aspects, whereas part two focuses on the underlying machine learning model and the data it uses.
  arXiv  Detail & Related papers  (2020-09-18T07:30:56Z)
• AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings [8.445274192818825]
  It is crucial for predictive models to be uncertainty-aware and yield trustworthy predictions. The focus of this symposium was on AI systems to improve data quality and technical robustness and safety. submissions from broadly defined areas also discussed approaches addressing requirements such as explainable models, human trust and ethical aspects of AI.
  arXiv  Detail & Related papers  (2020-01-15T15:30:29Z)

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.