# No computation without representation: Avoiding data and algorithm biases through diversity

arXiv: https://arxiv.org/abs/2002.11836 · HTML: https://mlsanity.com/paper/2002.11836/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Envisioning Communities: A Participatory Approach Towards AI for Social Good](https://mlsanity.com/paper/2105.01774/) (2021)
- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing](https://mlsanity.com/paper/2304.02017/) (2023)
- [Analyzing the Impact of Companies on AI Research Based on Publications](https://mlsanity.com/paper/2310.20444/) (2023)
- [Ethical Framework for Responsible Foundational Models in Medical Imaging](https://mlsanity.com/paper/2406.11868/) (2024)
- [CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretation](https://mlsanity.com/paper/2604.26288/) (2026)
- [Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias](https://mlsanity.com/paper/2512.05714/) (2025)

## Abstract (arXiv)

The emergence and growth of research on issues of ethics in AI, and in particular algorithmic fairness, has roots in an essential observation that structural inequalities in society are reflected in the data used to train predictive models and in the design of objective functions. While research aiming to mitigate these issues is inherently interdisciplinary, the design of unbiased algorithms and fair socio-technical systems are key desired outcomes which depend on practitioners from the fields of data science and computing. However, these computing fields broadly also suffer from the same under-representation issues that are found in the datasets we analyze. This disconnect affects the design of both the desired outcomes and metrics by which we measure success. If the ethical AI research community accepts this, we tacitly endorse the status quo and contradict the goals of non-discrimination and equity which work on algorithmic fairness, accountability, and transparency seeks to address. Therefore, we advocate in this work for diversifying computing as a core priority of the field and our efforts to achieve ethical AI practices. We draw connections between the lack of diversity within academic and professional computing fields and the type and breadth of the biases encountered in datasets, machine learning models, problem formulations, and interpretation of results. Examining the current fairness/ethics in AI literature, we highlight cases where this lack of diverse perspectives has been foundational to the inequity in treatment of underrepresented and protected group data. We also look to other professional communities, such as in law and health, where disparities have been reduced both in the educational diversity of trainees and among professional practices. We use these lessons to develop recommendations that provide concrete steps for the computing community to increase diversity.