Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices

• URL: http://arxiv.org/abs/2006.12358v4
• Date: Wed, 3 Mar 2021 01:59:43 GMT
• Title: Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices
• Authors: Bogdana Rakova, Jingying Yang, Henriette Cramer, Rumman Chowdhury
• Abstract summary: This paper examines and seeks to offer a framework for analyzing how organizational culture and structure impact the effectiveness of responsible AI initiatives in practice. We present the results of semi-structured qualitative interviews with practitioners working in industry, investigating common challenges, ethical tensions, and effective enablers for responsible AI initiatives.
• Score: 3.119859292303396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large and ever-evolving technology companies continue to invest more time and resources to incorporate responsible Artificial Intelligence (AI) into production-ready systems to increase algorithmic accountability. This paper examines and seeks to offer a framework for analyzing how organizational culture and structure impact the effectiveness of responsible AI initiatives in practice. We present the results of semi-structured qualitative interviews with practitioners working in industry, investigating common challenges, ethical tensions, and effective enablers for responsible AI initiatives. Focusing on major companies developing or utilizing AI, we have mapped what organizational structures currently support or hinder responsible AI initiatives, what aspirational future processes and structures would best enable effective initiatives, and what key elements comprise the transition from current work practices to the aspirational future.

Related papers

• Generative AI Application for Building Industry [10.154329382433213]
  This paper investigates the transformative potential of generative AI technologies, particularly large language models (LLMs) in the building industry. The research highlights how LLMs can automate labor-intensive processes, significantly improving efficiency, accuracy, and safety in building practices.
  arXiv  Detail & Related papers  (2024-10-01T21:59:08Z)
• Do Responsible AI Artifacts Advance Stakeholder Goals? Four Key Barriers Perceived by Legal and Civil Stakeholders [59.17981603969404]
  The responsible AI (RAI) community has introduced numerous processes and artifacts to facilitate transparency and support the governance of AI systems. We conduct semi-structured interviews with 19 government, legal, and civil society stakeholders who inform policy and advocacy around responsible AI efforts. We organize these beliefs into four barriers that help explain how RAI artifacts may (inadvertently) reconfigure power relations across civil society, government, and industry.
  arXiv  Detail & Related papers  (2024-08-22T00:14:37Z)
• Strategic AI adoption in SMEs: A Prescriptive Framework [0.0]
  The adoption of AI technologies in SMEs faces significant barriers, primarily related to cost, lack of technical skills, and employee acceptance. This study proposes a comprehensive, phased framework designed to facilitate the effective adoption of AI in SMEs.
  arXiv  Detail & Related papers  (2024-08-05T09:49:37Z)
• 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)
• AI Sustainability in Practice Part Two: Sustainability Throughout the AI Workflow [0.46671368497079174]
  This workbook is part two of two workbooks on AI Sustainability. It provides a template of the SIA and activities that allow a deeper dive into crucial parts of it. It discusses methods for weighing values and considering trade-offs during the SIA.
  arXiv  Detail & Related papers  (2024-02-19T22:58:05Z)
• Investigating Responsible AI for Scientific Research: An Empirical Study [4.597781832707524]
  The push for Responsible AI (RAI) in such institutions underscores the increasing emphasis on integrating ethical considerations within AI design and development. This paper aims to assess the awareness and preparedness regarding the ethical risks inherent in AI design and development. Our results have revealed certain knowledge gaps concerning ethical, responsible, and inclusive AI, with limitations in awareness of the available AI ethics frameworks.
  arXiv  Detail & Related papers  (2023-12-15T06:40:27Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" [76.44130385507894]
  This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
  arXiv  Detail & Related papers  (2021-11-01T17:57:04Z)
• 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)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• 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.