# Institutionalising Ethics in AI through Broader Impact Requirements

arXiv: https://arxiv.org/abs/2106.11039 · HTML: https://mlsanity.com/paper/2106.11039/

## 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 (most cited first, 2 of 2)

- [Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research](https://mlsanity.com/paper/2006.04541/) (2020)
- [Encore: Lightweight Measurement of Web Censorship with Cross-Origin Requests](https://mlsanity.com/paper/1410.1211/) (2014)

## Built on by (most cited first, 20 of 20)

- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling](https://mlsanity.com/paper/2107.03451/) (2021)
- [Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support](https://mlsanity.com/paper/2112.05675/) (2021)
- [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024)
- [Fairness Indicators for Systematic Assessments of Visual Feature Extractors](https://mlsanity.com/paper/2202.07603/) (2022)
- [Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey](https://mlsanity.com/paper/2211.05617/) (2022)
- [Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots](https://mlsanity.com/paper/2306.03823/) (2023)
- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statements](https://mlsanity.com/paper/2111.01705/) (2021)
- [Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?](https://mlsanity.com/paper/2303.09377/) (2023)
- [Towards Responsible Natural Language Annotation for the Varieties of Arabic](https://mlsanity.com/paper/2203.09597/) (2022)
- [ChatGPT: Vision and Challenges](https://mlsanity.com/paper/2305.15323/) (2023)
- [Making Intelligence: Ethical Values in IQ and ML Benchmarks](https://mlsanity.com/paper/2209.00692/) (2022)
- [Responsible AI Research Needs Impact Statements Too](https://mlsanity.com/paper/2311.11776/) (2023)
- [Artificial intelligence, rationalization, and the limits of control in the public sector: the case of tax policy optimization](https://mlsanity.com/paper/2407.05336/) (2024)
- [Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialogue](https://mlsanity.com/paper/2208.09174/) (2022)
- [Don't "research fast and break things": On the ethics of Computational Social Science](https://mlsanity.com/paper/2206.06370/) (2022)
- [Societal AI Research Has Become Less Interdisciplinary](https://mlsanity.com/paper/2506.08738/) (2025)
- [Emergent evaluation hubs in a decentralizing large language model ecosystem](https://mlsanity.com/paper/2510.01286/) (2025)
- [Culturally-Grounded Governance for Multilingual Language Models: Rights, Data Boundaries, and Accountable AI Design](https://mlsanity.com/paper/2602.00497/) (2026)

## 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.