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Institutionalising Ethics in AI through Broader Impact Requirements

arXiv 2106.11039 ↗First posted 30 May 2021cs.CY cs.AIcited by 20

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Abstract (arXiv)

Turning principles into practice is one of the most pressing challenges of artificial intelligence (AI) governance. In this article, we reflect on a novel governance initiative by one of the world's largest AI conferences. In 2020, the Conference on Neural Information Processing Systems (NeurIPS) introduced a requirement for submitting authors to include a statement on the broader societal impacts of their research. Drawing insights from similar governance initiatives, including institutional review boards (IRBs) and impact requirements for funding applications, we investigate the risks, challenges and potential benefits of such an initiative. Among the challenges, we list a lack of recognised best practice and procedural transparency, researcher opportunity costs, institutional and social pressures, cognitive biases, and the inherently difficult nature of the task. The potential benefits, on the other hand, include improved anticipation and identification of impacts, better communication with policy and governance experts, and a general strengthening of the norms around responsible research. To maximise the chance of success, we recommend measures to increase transparency, improve guidance, create incentives to engage earnestly with the process, and facilitate public deliberation on the requirement's merits and future. Perhaps the most important contribution from this analysis are the insights we can gain regarding effective community-based governance and the role and responsibility of the AI research community more broadly.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 2 of its 58 references that are in the map. First posted on arXiv 30 May 2021.

Builds on

2 of 58 references.

References · 2

  1. 2020Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Researchcited by 6
  2. 2014Encore: Lightweight Measurement of Web Censorship with Cross-Origin Requestscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 5; 2024: 2; 2025: 2; 2026: 1.

20 papers.

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  1. 2022Challenges and Strategies in Cross-Cultural NLPcited by 218
  2. 2021Anticipating Safety Issues in E2E Conversational AI: Framework and Toolingcited by 78
  3. 2021Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Supportcited by 63
  4. 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
  5. 2022Fairness Indicators for Systematic Assessments of Visual Feature Extractorscited by 24
  6. 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
  7. 2023Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbotscited by 18
  8. 2023Ethical Considerations for Responsible Data Curationcited by 17
  9. 2021AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statementscited by 16
  10. 2023Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?cited by 16
  11. 2022Towards Responsible Natural Language Annotation for the Varieties of Arabiccited by 14
  12. 2023ChatGPT: Vision and Challengescited by 11
8 more
  1. 2022Making Intelligence: Ethical Values in IQ and ML Benchmarkscited by 10
  2. 2023Responsible AI Research Needs Impact Statements Toocited by 7
  3. 2024Artificial intelligence, rationalization, and the limits of control in the public sector: the case of tax policy optimizationcited by 3
  4. 2022Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialoguecited by 1
  5. 2022Don't "research fast and break things": On the ethics of Computational Social Sciencecited by 0
  6. 2025Societal AI Research Has Become Less Interdisciplinarycited by 0
  7. 2025Emergent evaluation hubs in a decentralizing large language model ecosystemcited by 0
  8. 2026Culturally-Grounded Governance for Multilingual Language Models: Rights, Data Boundaries, and Accountable AI Designcited by 0