Putting AI Ethics into Practice: The Hourglass Model of Organizational AI Governance

• URL: http://arxiv.org/abs/2206.00335v2
• Date: Tue, 31 Jan 2023 10:32:13 GMT
• Title: Putting AI Ethics into Practice: The Hourglass Model of Organizational AI Governance
• Authors: Matti M\"antym\"aki, Matti Minkkinen, Teemu Birkstedt, Mika Viljanen
• Abstract summary: We present an AI governance framework, which targets organizations that develop and use AI systems. The framework is designed to help organizations deploying AI systems translate ethical AI principles into practice.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The organizational use of artificial intelligence (AI) has rapidly spread across various sectors. Alongside the awareness of the benefits brought by AI, there is a growing consensus on the necessity of tackling the risks and potential harms, such as bias and discrimination, brought about by advanced AI technologies. A multitude of AI ethics principles have been proposed to tackle these risks, but the outlines of organizational processes and practices for ensuring socially responsible AI development are in a nascent state. To address the paucity of comprehensive governance models, we present an AI governance framework, the hourglass model of organizational AI governance, which targets organizations that develop and use AI systems. The framework is designed to help organizations deploying AI systems translate ethical AI principles into practice and align their AI systems and processes with the forthcoming European AI Act. The hourglass framework includes governance requirements at the environmental, organizational, and AI system levels. At the AI system level, we connect governance requirements to AI system life cycles to ensure governance throughout the system's life span. The governance model highlights the systemic nature of AI governance and opens new research avenues into its practical implementation, the mechanisms that connect different AI governance layers, and the dynamics between the AI governance actors. The model also offers a starting point for organizational decision-makers to consider the governance components needed to ensure social acceptability, mitigate risks, and realize the potential of AI.

Related papers

• 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)
• Challenges and Best Practices in Corporate AI Governance:Lessons from the Biopharmaceutical Industry [0.0]
  We discuss challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance. We hope to provide project managers, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks with general best practices.
  arXiv  Detail & Related papers  (2024-07-07T12:01:42Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• POLARIS: A framework to guide the development of Trustworthy AI systems [3.02243271391691]
  There is a significant gap between high-level AI ethics principles and low-level concrete practices for AI professionals. We develop a novel holistic framework for Trustworthy AI - designed to bridge the gap between theory and practice. Our goal is to empower AI professionals to confidently navigate the ethical dimensions of Trustworthy AI.
  arXiv  Detail & Related papers  (2024-02-08T01:05:16Z)
• 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)
• Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering [20.644494592443245]
  We present a Responsible AI Pattern Catalogue based on the results of a Multivocal Literature Review (MLR) Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle.
  arXiv  Detail & Related papers  (2022-09-12T00:09:08Z)
• Governance of Ethical and Trustworthy AI Systems: Research Gaps in the ECCOLA Method [5.28595286827031]
  This research analyzes the ECCOLA method for developing ethical and trustworthy AI systems. The results demonstrate that while ECCOLA fully facilitates AI governance in corporate governance practices in all its processes, some of its practices do not fully foster data governance and information governance practices.
  arXiv  Detail & Related papers  (2021-11-11T13:54:31Z)
• 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)
• 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)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)

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.