# Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies

**Authors:** Emilio Ferrara  
**arXiv:** [2304.07683](https://arxiv.org/abs/2304.07683)  
**Date:** 2023-04-16  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2304.07683v2/

## Key points

- Fairness and bias in AI applications are critical concerns in sectors such as healthcare, employment, criminal justice, credit scoring, and synthetic media creation.
- Sources of bias in AI systems include data biases, algorithmic biases, and biases from human decision-making processes.
- Generative AI models can inadvertently reinforce societal stereotypes, leading to biased outcomes that exacerbate inequalities and harmful stereotypes.
- Mitigation strategies for AI bias focus on ethical considerations and interdisciplinary collaboration to ensure effectiveness.
- Current approaches to mitigate bias include data pre-processing, model selection, and post-processing techniques tailored to address the unique challenges posed by generative AI models.

## Summary

, , , , 

In the rapidly evolving landscape of Artificial Intelligence (AI) applications, concerns regarding fairness and bias have come to the forefront. This issue is especially critical in sectors like healthcare, employment, criminal justice, credit scoring, and the creation of synthetic media through generative AI models. The potential for AI systems to produce biased outcomes and perpetuate existing inequalities poses significant challenges that must be addressed. This survey paper provides a comprehensive overview of fairness and bias in AI, delving into their sources, impacts, and mitigation strategies. The paper examines various sources of bias including data biases, algorithmic biases, and biases stemming from human decision-making processes. Of particular concern is the emergent issue of generative AI bias where models may inadvertently reinforce societal stereotypes. The societal impact of biased AI systems is profound as they can exacerbate inequalities and reinforce harmful stereotypes. This is especially concerning as generative AI becomes more prevalent in shaping public perception through content creation. The paper explores proposed mitigation strategies with a focus on ethical considerations and emphasizes the importance of interdisciplinary collaboration to ensure their effectiveness. Through a systematic literature review spanning multiple academic disciplines, it defines AI bias and its different types while providing a detailed analysis of generative AI bias. It highlights the negative impacts of biased AI on individuals and society at large while offering an overview of current approaches to mitigate bias such as data pre-processing, model selection, and post-processing techniques. The unique challenges posed by generative AI models underscore the need for tailored strategies specifically designed to address these issues effectively. By addressing fairness and bias in AI systems comprehensively, this paper contributes valuable insights towards creating more equitable and unbiased artificial intelligence technologies across various domains.