Summary of Whither Bias Goes, I Will Go: An Integrative, Systematic Review Of Algorithmic Bias Mitigation, by Louis Hickman et al.
Whither Bias Goes, I Will Go: An Integrative, Systematic Review of Algorithmic Bias Mitigation
by Louis Hickman, Christopher Huynh, Jessica Gass, Brandon Booth, Jason Kuruzovich, Louis Tay
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a four-stage model for developing machine learning (ML) assessments that takes into account the potential sources of bias and unfairness at each stage. The model includes generating training data, training the model, testing the model, and deploying the model. The authors also review definitions and operationalizations of algorithmic bias, legal requirements governing personnel selection in the United States and Europe, and research on algorithmic bias mitigation across multiple domains. The framework provides insights for both research and practice by elucidating possible mechanisms of algorithmic bias while identifying which bias mitigation methods are legal and effective. Furthermore, the paper highlights gaps in the knowledge of algorithmic bias mitigation that should be addressed by future collaborative research between organizational researchers, computer scientists, and data scientists. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machine learning models can be used to assess people for jobs or other opportunities. Some people worry that these models might be biased and unfair, which could make things worse for certain groups of people. The authors want to understand and fix this problem by creating a four-step plan for making sure the models are fair and unbiased. They also look at how laws in different countries handle personnel selection and what researchers have found about reducing bias in algorithms. |
Keywords
* Artificial intelligence * Machine learning
