The Equitable AI Research Roundtable (EARR): Towards Community-Based Decision Making in Responsible AI Development

• URL: http://arxiv.org/abs/2303.08177v1
• Date: Tue, 14 Mar 2023 18:57:20 GMT
• Title: The Equitable AI Research Roundtable (EARR): Towards Community-Based Decision Making in Responsible AI Development
• Authors: Jamila Smith-Loud, Andrew Smart, Darlene Neal, Amber Ebinama, Eric Corbett, Paul Nicholas, Qazi Rashid, Anne Peckham, Sarah Murphy-Gray, Nicole Morris, Elisha Smith Arrillaga, Nicole-Marie Cotton, Emnet Almedom, Olivia Araiza, Eliza McCullough, Abbie Langston, Christopher Nellum
• Abstract summary: The paper reports on our initial evaluation of The Equitable AI Research Roundtable. EARR was created in collaboration among a large tech firm, nonprofits, NGO research institutions, and universities. We outline three principles in practice of how EARR has operated thus far that are especially relevant to the concerns of the FAccT community.
• Score: 4.1986677342209004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper reports on our initial evaluation of The Equitable AI Research Roundtable -- a coalition of experts in law, education, community engagement, social justice, and technology. EARR was created in collaboration among a large tech firm, nonprofits, NGO research institutions, and universities to provide critical research based perspectives and feedback on technology's emergent ethical and social harms. Through semi-structured workshops and discussions within the large tech firm, EARR has provided critical perspectives and feedback on how to conceptualize equity and vulnerability as they relate to AI technology. We outline three principles in practice of how EARR has operated thus far that are especially relevant to the concerns of the FAccT community: how EARR expands the scope of expertise in AI development, how it fosters opportunities for epistemic curiosity and responsibility, and that it creates a space for mutual learning. This paper serves as both an analysis and translation of lessons learned through this engagement approach, and the possibilities for future research.

Related papers

• The Transformative Impact of AI and Deep Learning in Business: A Literature Review [0.0]
  This paper aims to review the radical role of AI and deep learning in various functional areas of the business. It covers material applications in the healthcare sector, the retail and manufacturing industry, agriculture and farming, and finance.
  arXiv  Detail & Related papers  (2024-10-30T20:35:03Z)
• 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)
• Integrating ESG and AI: A Comprehensive Responsible AI Assessment Framework [15.544366555353262]
  ESG-AI framework was developed based on insights from engagements with 28 companies. It provides an overview of the environmental and social impacts of AI applications, helping users such as investors assess the materiality of AI use. It enables investors to evaluate a company's commitment to responsible AI through structured engagements and thorough assessment of specific risk areas.
  arXiv  Detail & Related papers  (2024-08-02T00:58:01Z)
• Using Case Studies to Teach Responsible AI to Industry Practitioners [8.152080071643685]
  We propose a novel stakeholder-first educational approach that uses interactive case studies to achieve organizational and practitioner -level engagement and advance learning of Responsible AI (RAI) Our assessment results indicate that participants found the workshops engaging and reported a positive shift in understanding and motivation to apply RAI to their work.
  arXiv  Detail & Related papers  (2024-07-19T22:06:06Z)
• 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)
• Responsible AI Considerations in Text Summarization Research: A Review of Current Practices [89.85174013619883]
  We focus on text summarization, a common NLP task largely overlooked by the responsible AI community. We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022. We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals.
  arXiv  Detail & Related papers  (2023-11-18T15:35:36Z)
• The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice [64.29355073494125]
  This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation. We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
  arXiv  Detail & Related papers  (2023-10-02T05:30:42Z)
• Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions [59.34177693293227]
  We explore the applicability of responsible, ethical, and trustworthy AI within the context of manufacturing. We then use a broadened adaptation of a machine learning lifecycle to discuss, through the use of illustrative examples, how each step may result in a given AI trustworthiness concern.
  arXiv  Detail & Related papers  (2023-05-19T10:43:06Z)
• 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)
• 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)
• Progressing Towards Responsible AI [2.191505742658975]
  Observatory on Society and Artificial Intelligence (OSAI) grew out of the project AI4EU. OSAI aims to stimulate reflection on a broad spectrum of issues of AI (ethical, legal, social, economic and cultural)
  arXiv  Detail & Related papers  (2020-08-11T09:46:00Z)

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.