# AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statements arXiv: https://arxiv.org/abs/2111.01705 ยท HTML: https://mlsanity.com/paper/2111.01705/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 13 of its 50 references that are in the map. - First posted on arXiv 2 Nov 2021. ## Builds on (most cited first, 13 of 13) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [What is being transferred in transfer learning?](https://mlsanity.com/paper/2008.11687/) (2020) - [Graph Information Bottleneck](https://mlsanity.com/paper/2010.12811/) (2020) - [COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning](https://mlsanity.com/paper/2011.00597/) (2020) - [Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration](https://mlsanity.com/paper/2002.09253/) (2020) - [Overcoming Failures of Imagination in AI Infused System Development and Deployment](https://mlsanity.com/paper/2011.13416/) (2020) - [Unpacking the Expressed Consequences of AI Research in Broader Impact Statements](https://mlsanity.com/paper/2105.04760/) (2021) - [Institutionalising Ethics in AI through Broader Impact Requirements](https://mlsanity.com/paper/2106.11039/) (2021) - [Sufficient dimension reduction for classification using principal optimal transport direction](https://mlsanity.com/paper/2010.09921/) (2020) - [Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research](https://mlsanity.com/paper/2006.04541/) (2020) - [ESR: Ethics and Society Review of Artificial Intelligence Research](https://mlsanity.com/paper/2106.11521/) (2021) - [Like a Researcher Stating Broader Impact For the Very First Time](https://mlsanity.com/paper/2011.13032/) (2020) - [Adaptive Reduced Rank Regression](https://mlsanity.com/paper/1905.11566/) (2019) ## Built on by (most cited first, 16 of 16) - [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024) - [Navigating LLM Ethics: Advancements, Challenges, and Future Directions](https://mlsanity.com/paper/2406.18841/) (2024) - [AnthroScore: A Computational Linguistic Measure of Anthropomorphism](https://mlsanity.com/paper/2402.02056/) (2024) - [Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey](https://mlsanity.com/paper/2211.05617/) (2022) - [Automated Few-shot Classification with Instruction-Finetuned Language Models](https://mlsanity.com/paper/2305.12576/) (2023) - [Responsible AI Research Needs Impact Statements Too](https://mlsanity.com/paper/2311.11776/) (2023) - [Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor](https://mlsanity.com/paper/2506.14652/) (2025) - [BLIP: Facilitating the Exploration of Undesirable Consequences of Digital Technologies](https://mlsanity.com/paper/2405.06783/) (2024) - [Responsible AI Considerations in Text Summarization Research: A Review of Current Practices](https://mlsanity.com/paper/2311.11103/) (2023) - [Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialogue](https://mlsanity.com/paper/2208.09174/) (2022) - [From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap](https://mlsanity.com/paper/2404.13131/) (2024) - [Don't "research fast and break things": On the ethics of Computational Social Science](https://mlsanity.com/paper/2206.06370/) (2022) - [Inaccessible Neural Language Models Could Reinvigorate Linguistic Nativism](https://mlsanity.com/paper/2301.05272/) (2023) - [Recognition Without Mitigation: Ethical Frameworks in Autonomous Offensive-LLM Agent Research](https://mlsanity.com/paper/2506.08693/) (2025) - [Societal AI Research Has Become Less Interdisciplinary](https://mlsanity.com/paper/2506.08738/) (2025) - [Position: The Alignment Community is Unintentionally Building a Censor's Toolkit](https://mlsanity.com/paper/2608.12346/) (2026) ## Abstract (arXiv) Ethics statements have been proposed as a mechanism to increase transparency and promote reflection on the societal impacts of published research. 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. The 2020 statements therefore provide a unique opportunity to learn from the broader impact experiment: to investigate the benefits and challenges of this and similar governance mechanisms, as well as providing an insight into how ML researchers think about the societal impacts of their own work. Such learning is needed as NeurIPS and other venues continue to question and adapt their policies. To enable this, we have created a dataset containing the impact statements from all NeurIPS 2020 papers, along with additional information such as affiliation type, location and subject area, and a simple visualisation tool for exploration. We also provide an initial quantitative analysis of the dataset, covering representation, engagement, common themes, and willingness to discuss potential harms alongside benefits. We investigate how these vary by geography, affiliation type and subject area. Drawing on these findings, we discuss the potential benefits and negative outcomes of ethics statement requirements, and their possible causes and associated challenges. These lead us to several lessons to be learnt from the 2020 requirement: (i) the importance of creating the right incentives, (ii) the need for clear expectations and guidance, and (iii) the importance of transparency and constructive deliberation. We encourage other researchers to use our dataset to provide additional analysis, to further our understanding of how researchers responded to this requirement, and to investigate the benefits and challenges of this and related mechanisms.