# Data, Power and Bias in Artificial Intelligence

arXiv: https://arxiv.org/abs/2008.07341 · HTML: https://mlsanity.com/paper/2008.07341/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process](https://mlsanity.com/paper/2506.01080/) (2025)
- [SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasets](https://mlsanity.com/paper/2111.06467/) (2021)
- [Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems](https://mlsanity.com/paper/2201.06952/) (2022)
- [Truth Machines: Synthesizing Veracity in AI Language Models](https://mlsanity.com/paper/2301.12066/) (2023)
- [AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems](https://mlsanity.com/paper/2311.14695/) (2023)
- [Investigating Responsible AI for Scientific Research: An Empirical Study](https://mlsanity.com/paper/2312.09561/) (2023)
- [Understanding and Estimating Domain Complexity Across Domains](https://mlsanity.com/paper/2312.13487/) (2023)

## Abstract (arXiv)

Artificial Intelligence has the potential to exacerbate societal bias and set back decades of advances in equal rights and civil liberty. Data used to train machine learning algorithms may capture social injustices, inequality or discriminatory attitudes that may be learned and perpetuated in society. Attempts to address this issue are rapidly emerging from different perspectives involving technical solutions, social justice and data governance measures. While each of these approaches are essential to the development of a comprehensive solution, often discourse associated with each seems disparate. This paper reviews ongoing work to ensure data justice, fairness and bias mitigation in AI systems from different domains exploring the interrelated dynamics of each and examining whether the inevitability of bias in AI training data may in fact be used for social good. We highlight the complexity associated with defining policies for dealing with bias. We also consider technical challenges in addressing issues of societal bias.