Summary of A Survey Of Recent Methods For Addressing Ai Fairness and Bias in Biomedicine, by Yifan Yang et al.
A survey of recent methods for addressing AI fairness and bias in biomedicine
by Yifan Yang, Mingquan Lin, Han Zhao, Yifan Peng, Furong Huang, Zhiyong Lu
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed paper explores the potential biases in artificial intelligence (AI) models used in clinical settings, such as improving diagnostic accuracy and surgical decision-making. The authors recognize that these systems may perpetuate social inequities or demonstrate biases based on race or gender, emphasizing the importance of understanding and addressing potential biases to ensure accurate and reliable applications. To mitigate bias concerns during model development, the authors surveyed recent publications on debiasing methods in biomedical natural language processing (NLP) and computer vision (CV). They then discussed the methods applied in the biomedical domain to address bias, highlighting strengths and weaknesses. The paper also reviews potential methods from the general domain that could be applied to biomedicine to improve fairness and reduce biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence can help doctors make better decisions and save time and money. But AI systems might not always be fair or accurate, especially if they’re based on old or biased data. This paper looks at how AI models are developed and used in medicine, and what we can do to make them more fair and reliable. The authors studied different ways to “debias” AI models, which means making sure they don’t favor one group over another. They looked at methods that have been tried before and talked about their strengths and weaknesses. This research can help us create better AI systems that work for everyone. |
Keywords
* Artificial intelligence * Natural language processing * Nlp
