Institutionalising Ethics in AI through Broader Impact Requirements
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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.
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Citing papers per year: 2021: 3; 2022: 7; 2023: 5; 2024: 2; 2025: 2; 2026: 1.
20 papers.
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- 2022Challenges and Strategies in Cross-Cultural NLPcited by 218
- 2021Anticipating Safety Issues in E2E Conversational AI: Framework and Toolingcited by 78
- 2021Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Supportcited by 63
- 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
- 2022Fairness Indicators for Systematic Assessments of Visual Feature Extractorscited by 24
- 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
- 2023Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbotscited by 18
- 2023Ethical Considerations for Responsible Data Curationcited by 17
- 2021AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statementscited by 16
- 2023Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?cited by 16
- 2022Towards Responsible Natural Language Annotation for the Varieties of Arabiccited by 14
- 2023ChatGPT: Vision and Challengescited by 11
8 more
- 2022Making Intelligence: Ethical Values in IQ and ML Benchmarkscited by 10
- 2023Responsible AI Research Needs Impact Statements Toocited by 7
- 2024Artificial intelligence, rationalization, and the limits of control in the public sector: the case of tax policy optimizationcited by 3
- 2022Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialoguecited by 1
- 2022Don't "research fast and break things": On the ethics of Computational Social Sciencecited by 0
- 2025Societal AI Research Has Become Less Interdisciplinarycited by 0
- 2025Emergent evaluation hubs in a decentralizing large language model ecosystemcited by 0
- 2026Culturally-Grounded Governance for Multilingual Language Models: Rights, Data Boundaries, and Accountable AI Designcited by 0
- 2026Culturally-Grounded Governance for Multilingual Language Models: Rights, Data Boundaries, and Accountable AI Design31 Jan 2026
- 2025Emergent evaluation hubs in a decentralizing large language model ecosystem30 Sep 2025
- 2025Societal AI Research Has Become Less Interdisciplinary10 Jun 2025
- 2024Artificial intelligence, rationalization, and the limits of control in the public sector: the case of tax policy optimization7 Jul 2024
- 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototyping23 Feb 2024
- 2023Responsible AI Research Needs Impact Statements Too20 Nov 2023
- 2023Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots25 May 2023
- 2023ChatGPT: Vision and Challenges8 May 2023
- 2023Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?16 Mar 2023
- 2023Ethical Considerations for Responsible Data Curation7 Feb 2023
- 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey10 Nov 2022
- 2022Making Intelligence: Ethical Values in IQ and ML Benchmarks1 Sep 2022
8 more
- 2022Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialogue19 Aug 2022
- 2022Don't "research fast and break things": On the ethics of Computational Social Science12 Jun 2022
- 2022Challenges and Strategies in Cross-Cultural NLP18 Mar 2022
- 2022Towards Responsible Natural Language Annotation for the Varieties of Arabic17 Mar 2022
- 2022Fairness Indicators for Systematic Assessments of Visual Feature Extractors15 Feb 2022
- 2021Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support10 Dec 2021
- 2021AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statements2 Nov 2021
- 2021Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling7 Jul 2021
- 2022Challenges and Strategies in Cross-Cultural NLPcited by 218
- 2021Anticipating Safety Issues in E2E Conversational AI: Framework and Toolingcited by 78
- 2021Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Supportcited by 63
- 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
- 2022Fairness Indicators for Systematic Assessments of Visual Feature Extractorscited by 24
- 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
- 2023Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbotscited by 18
- 2023Ethical Considerations for Responsible Data Curationcited by 17
- 2021AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statementscited by 16
- 2023Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?cited by 16
- 2022Towards Responsible Natural Language Annotation for the Varieties of Arabiccited by 14
- 2023ChatGPT: Vision and Challengescited by 11
12 most cited of 20 All 20 (JSON)
- 2026Culturally-Grounded Governance for Multilingual Language Models: Rights, Data Boundaries, and Accountable AI Design31 Jan 2026
- 2025Emergent evaluation hubs in a decentralizing large language model ecosystem30 Sep 2025
- 2025Societal AI Research Has Become Less Interdisciplinary10 Jun 2025
- 2024Artificial intelligence, rationalization, and the limits of control in the public sector: the case of tax policy optimization7 Jul 2024
- 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototyping23 Feb 2024
- 2023Responsible AI Research Needs Impact Statements Too20 Nov 2023
- 2023Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots25 May 2023
- 2023ChatGPT: Vision and Challenges8 May 2023
- 2023Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?16 Mar 2023
- 2023Ethical Considerations for Responsible Data Curation7 Feb 2023
- 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey10 Nov 2022
- 2022Making Intelligence: Ethical Values in IQ and ML Benchmarks1 Sep 2022
12 newest of 20 All 20 (JSON)