Summary of Revisiting Technical Bias Mitigation Strategies, by Abdoul Jalil Djiberou Mahamadou et al.
Revisiting Technical Bias Mitigation Strategies
by Abdoul Jalil Djiberou Mahamadou, Artem A. Trotsyuk
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers delve into the challenges of mitigating bias and enhancing fairness in artificial intelligence (AI) for healthcare applications. The review focuses on the practical limitations of technical solutions, analyzing five key dimensions: who defines bias and fairness, which mitigation strategies to use, when to apply them, for which populations, and in what context. The authors illustrate each limitation with empirical studies in healthcare and biomedical settings. Additionally, they discuss value-sensitive AI, a framework that engages stakeholders to ensure their values are reflected in bias and fairness solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For high school students or non-technical adults, this paper is about making sure the artificial intelligence used in hospitals and clinics doesn’t unfairly treat people. The researchers looked at what makes these biases happen and how we can fix them. They found that there are many ways to try to fix bias, but some work better than others depending on who’s using it and when. They also talked about involving doctors, patients, and other experts in the process so that everyone’s values are considered. |
