Summary of The Neutrality Fallacy: When Algorithmic Fairness Interventions Are (not) Positive Action, by Hilde Weerts et al.
The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action
by Hilde Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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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 A machine learning study proposes reinterpreting fairness-aware interventions as means to prevent discrimination rather than measures of positive action. The researchers suggest that these interventions can often be attributed to neutrality fallacies, faulty assumptions regarding the neutrality of fairness-aware algorithmic decision-making. They argue that a negative obligation to refrain from discrimination is insufficient in the context of algorithmic decision-making and propose moving towards a positive obligation to actively “do no harm” as a more adequate framework for fair ML-interventions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning experts are working on making sure AI systems don’t discriminate. Some think this means they have to take extra steps to help certain groups. But what if we’re thinking about it wrong? The paper says maybe we should just focus on not hurting anyone, rather than trying to help specific groups. This could change how we approach fairness in AI. |
Keywords
» Artificial intelligence » Machine learning
