# Unpacking the Expressed Consequences of AI Research in Broader Impact Statements

arXiv: https://arxiv.org/abs/2105.04760 · HTML: https://mlsanity.com/paper/2105.04760/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2021.

## Built on by (most cited first, 22 of 22)

- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM](https://mlsanity.com/paper/2404.12259/) (2024)
- [How Different Groups Prioritize Ethical Values for Responsible AI](https://mlsanity.com/paper/2205.07722/) (2022)
- [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024)
- [Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey](https://mlsanity.com/paper/2211.05617/) (2022)
- [REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research](https://mlsanity.com/paper/2205.08363/) (2022)
- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statements](https://mlsanity.com/paper/2111.01705/) (2021)
- [Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review](https://mlsanity.com/paper/2501.12557/) (2025)
- [Metaethical Perspectives on 'Benchmarking' AI Ethics](https://mlsanity.com/paper/2204.05151/) (2022)
- [Responsible AI Research Needs Impact Statements Too](https://mlsanity.com/paper/2311.11776/) (2023)
- [BLIP: Facilitating the Exploration of Undesirable Consequences of Digital Technologies](https://mlsanity.com/paper/2405.06783/) (2024)
- [Use of Formal Ethical Reviews in NLP Literature: Historical Trends and Current Practices](https://mlsanity.com/paper/2106.01105/) (2021)
- [Responsible AI Considerations in Text Summarization Research: A Review of Current Practices](https://mlsanity.com/paper/2311.11103/) (2023)
- [A Systematic Review of NeurIPS Dataset Management Practices](https://mlsanity.com/paper/2411.00266/) (2024)
- [Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making](https://mlsanity.com/paper/2502.14869/) (2025)
- [Addressing Privacy Threats from Machine Learning](https://mlsanity.com/paper/2111.04439/) (2021)
- [Don't "research fast and break things": On the ethics of Computational Social Science](https://mlsanity.com/paper/2206.06370/) (2022)
- [Evaluating the Capabilities of LLMs for Supporting Anticipatory Impact Assessment](https://mlsanity.com/paper/2401.18028/) (2024)
- [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)

## Abstract (arXiv)

The computer science research community and the broader public have become increasingly aware of negative consequences of algorithmic systems. In response, the top-tier Neural Information Processing Systems (NeurIPS) conference for machine learning and artificial intelligence research required that authors include a statement of broader impact to reflect on potential positive and negative consequences of their work. We present the results of a qualitative thematic analysis of a sample of statements written for the 2020 conference. The themes we identify broadly fall into categories related to how consequences are expressed (e.g., valence, specificity, uncertainty), areas of impacts expressed (e.g., bias, the environment, labor, privacy), and researchers' recommendations for mitigating negative consequences in the future. 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.