arXiv:2511.03304v2 Announce Type: replace Abstract: With the on-going integration of machine learning systems into the everyday social life of millions the notion of fairness becomes an ever increasing priority in their development. Fairness notions commonly rely on protected attributes to assess potential biases. Here, the majority of literature focuses on discrete setups regarding both target and protected attributes. The literature on continuous attributes especially in conjunction with regression -- we refer to this as \emph{continuous fairness} -- is scarce. A common strategy is iterative
The increasing integration of machine learning into daily life makes fairness a critical and immediate concern, prompting deeper exploration of bias in ML systems.
This research addresses a gap in fairness literature by extending methods to continuous attributes, crucial for building more ethical and robust AI systems across various applications.
The focus on 'continuous fairness' for regression problems expands the scope of algorithmic bias mitigation, moving beyond discrete protected attributes.
- · AI ethics researchers
- · Organizations deploying AI with sensitive data
- · Machine learning developers
- · Developers ignoring continuous fairness
- · AI systems with unmitigated continuous bias
Improved methods for detecting and mitigating continuous bias in machine learning models will become available.
The application of these methods could lead to fairer outcomes in areas like credit scoring, healthcare, and employment.
Increased public trust in AI systems may result from demonstrable fairness, accelerating their societal integration but also necessitating new regulatory frameworks.
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Read at arXiv cs.LG