Summary of (un)certainty Of (un)fairness: Preference-based Selection Of Certainly Fair Decision-makers, by Manh Khoi Duong et al.
(Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makers
by Manh Khoi Duong, Stefan Conrad
First submitted to arxiv on: 19 Sep 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 This paper focuses on enhancing fairness assessments in decision-making processes by quantifying uncertainty using Bayesian statistics. Traditional fairness metrics overlook the disparity’s uncertainty, leading to inconsistencies when comparing different decision-makers’ disparities. The authors represent each decision-maker (human or machine learning model) with its disparity and corresponding uncertainty. They define preferences over decision-makers and use a utility function to rank them based on these preferences. The optimal decision-maker is chosen according to this ranking, representing the one for which we are most certain it is fair. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make sure that decisions are fair by measuring how much different groups are affected differently. Right now, there’s a problem with traditional fairness measurements because they don’t account for uncertainty and inconsistencies when comparing decision-makers. The authors use special statistics called Bayesian statistics to solve this issue. They represent each decision-maker (human or machine learning model) as its effect on different groups and how certain we can be about that effect. Then, they choose the best decision-maker based on a set of preferences that prioritize fairness. |
Keywords
* Artificial intelligence * Machine learning
