# Mitigating Bias in Algorithmic Systems -- A Fish-Eye View

arXiv: https://arxiv.org/abs/2103.16953 · HTML: https://mlsanity.com/paper/2103.16953/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2021.

## Built on by (most cited first, 10 of 10)

- [Representation Bias in Data: A Survey on Identification and Resolution Techniques](https://mlsanity.com/paper/2203.11852/) (2022)
- [Survey on Fairness Notions and Related Tensions](https://mlsanity.com/paper/2209.13012/) (2022)
- [Racial Bias within Face Recognition: A Survey](https://mlsanity.com/paper/2305.00817/) (2023)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission](https://mlsanity.com/paper/2206.06279/) (2022)
- [Fake News Detection: It's All in the Data!](https://mlsanity.com/paper/2407.02122/) (2024)
- [Fairness-Aware Streaming Feature Selection with Causal Graphs](https://mlsanity.com/paper/2408.12665/) (2024)
- [Best Practices for Responsible Machine Learning in Credit Scoring](https://mlsanity.com/paper/2409.20536/) (2024)
- [Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization](https://mlsanity.com/paper/2111.03638/) (2021)
- [An Approach to Ensure Fairness in News Articles](https://mlsanity.com/paper/2207.03938/) (2022)

## Abstract (arXiv)

Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. Given the complexity of the problem and the involvement of multiple stakeholders -- including developers, end-users, and third parties -- there is a need to understand the landscape of the sources of bias, and the solutions being proposed to address them, from a broad, cross-domain perspective. This survey provides a "fish-eye view," examining approaches across four areas of research. The literature describes three steps toward a comprehensive treatment -- bias detection, fairness management and explainability management -- and underscores the need to work from within the system as well as from the perspective of stakeholders in the broader context.