Summary of Unbiasing on the Fly: Explanation-guided Human Oversight Of Machine Learning System Decisions, by Hussaini Mamman et al.
Unbiasing on the Fly: Explanation-Guided Human Oversight of Machine Learning System Decisions
by Hussaini Mamman, Shuib Basri, Abdullateef Balogun, Abubakar Abdullahi Imam, Ganesh Kumar, Luiz Fernando Capretz
First submitted to arxiv on: 25 Jun 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 novel framework continuously monitors the predictions made by an ML system and flags discriminatory outcomes, leveraging counterfactual explanations. This human-in-the-loop approach empowers reviewers to accept or override the ML system decision, enabling fair and responsible ML operation under dynamic settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make sure AI systems don’t discriminate has been proposed. Right now, AI is being used in important areas like hiring and healthcare, but it’s possible that these systems could be biased against certain groups of people. To fix this, a framework was created to check for biases while the AI system is working. If it finds something unfair, it gives humans a chance to review and change the decision. |
