Like a Researcher Stating Broader Impact For the Very First Time

• URL: http://arxiv.org/abs/2011.13032v1
• Date: Wed, 25 Nov 2020 21:32:29 GMT
• Title: Like a Researcher Stating Broader Impact For the Very First Time
• Authors: Grace Abuhamad and Claudel Rheault
• Abstract summary: This paper seeks to answer the question of how individual researchers reacted to the new requirement. We present survey results and considerations to inform the next iteration of the broader impact requirement should it remain a requirement for future NeurIPS conferences.
• Score: 3.2634122554914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In requiring that a statement of broader impact accompany all submissions for this year's conference, the NeurIPS program chairs made ethics part of the stake in groundbreaking AI research. While there is precedent from other fields and increasing awareness within the NeurIPS community, this paper seeks to answer the question of how individual researchers reacted to the new requirement, including not just their views, but also their experience in drafting and their reflections after paper acceptances. We present survey results and considerations to inform the next iteration of the broader impact requirement should it remain a requirement for future NeurIPS conferences.

Related papers

• An Undergraduate Consortium for Addressing the Leaky Pipeline to Computing Research [1.9336815376402718]
  This experience report describes a first-of-its-kind Undergraduate Consortium (UC) The UC aims to broaden participation in the AI research community by recruiting students, particularly those from historically marginalized groups. This paper presents our program design, inspired by a rich set of evidence-based practices, and a preliminary evaluation of the first years that points to the UC achieving many of its desired outcomes.
  arXiv  Detail & Related papers  (2024-03-25T21:43:43Z)
• 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)
• Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research [75.84463664853125]
  We provide a first attempt to quantify concerns regarding three topics, namely, environmental impact, equity, and impact on peer reviewing. We capture existing (dis)parities between different and within groups with respect to seniority, academia, and industry. We devise recommendations to mitigate found disparities, some of which already successfully implemented.
  arXiv  Detail & Related papers  (2023-06-29T12:44:53Z)
• Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment [86.77085171670323]
  We present a larger-scale variant of the 2014 NeurIPS experiment in which 10% of conference submissions were reviewed by two independent committees to quantify the randomness in the review process. We observe that the two committees disagree on their accept/reject recommendations for 23% of the papers and that, consistent with the results from 2014, approximately half of the list of accepted papers would change if the review process were randomly rerun.
  arXiv  Detail & Related papers  (2023-06-05T21:26:12Z)
• A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers [76.51245425667845]
  Relation extraction (RE) involves identifying the relations between entities from underlying content. Deep neural networks have dominated the field of RE and made noticeable progress. This survey is expected to facilitate researchers' collaborative efforts to address the challenges of real-world RE systems.
  arXiv  Detail & Related papers  (2023-06-03T08:39:25Z)
• The History of AI Rights Research [0.0]
  Report documents the history of research on AI rights and other moral consideration of artificial entities. It highlights key intellectual influences on this literature as well as research and academic discussion addressing the topic more directly.
  arXiv  Detail & Related papers  (2022-07-06T17:52:27Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• What Factors Should Paper-Reviewer Assignments Rely On? Community Perspectives on Issues and Ideals in Conference Peer-Review [20.8704278772718]
  We present the results of the first survey of the NLP community. We identify common issues and perspectives on what factors should be considered by paper-reviewer matching systems.
  arXiv  Detail & Related papers  (2022-05-02T16:07:02Z)
• AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statements [0.0]
  In 2020, the machine learning (ML) conference NeurIPS broke new ground by requiring that all papers include a broader impact statement. This requirement was removed in 2021, in favour of a checklist approach. We have created a dataset containing the impact statements from all NeurIPS 2020 papers.
  arXiv  Detail & Related papers  (2021-11-02T16:17:12Z)
• 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)
• Unpacking the Expressed Consequences of AI Research in Broader Impact Statements [23.3030110636071]
  We present the results of a thematic analysis of a sample of statements written for the 2020 Neural Information Processing Systems conference. The themes we identify fall into categories related to how consequences are expressed and areas of impacts expressed. In light of our results, we offer perspectives on how the broader impact statement can be implemented in future iterations to better align with potential goals.
  arXiv  Detail & Related papers  (2021-05-11T02:57:39Z)

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.