Summary of The Pursuit Of Fairness in Artificial Intelligence Models: a Survey, by Tahsin Alamgir Kheya and Mohamed Reda Bouadjenek and Sunil Aryal
The Pursuit of Fairness in Artificial Intelligence Models: A Survey
by Tahsin Alamgir Kheya, Mohamed Reda Bouadjenek, Sunil Aryal
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper surveys the current state of research on mitigating bias in Artificial Intelligence (AI) models, which are increasingly used in various domains such as healthcare, education, and employment. The authors highlight the importance of ensuring that these models do not manifest discriminatory practices like partiality towards certain genders, ethnicities, or disabled people. They present a comprehensive taxonomy of different types of bias and investigate cases of biased AI in various application domains. The paper also explores definitions of fairness existing in current literature and studies approaches and techniques employed to mitigate bias in AI models. Additionally, it discusses the impact of biased models on user experience and ethical considerations when developing and deploying such models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how researchers are working to make sure Artificial Intelligence (AI) systems don’t have biases that could affect people unfairly. This is important because AI is used in many areas like healthcare, education, and jobs. The authors show different types of bias that can happen with AI and give examples of when this has happened. They also talk about ways researchers are trying to fix these problems and what we need to think about when creating and using biased models. |
