arXiv:2509.21785v2 Announce Type: replace-cross Abstract: Discretizing raw features into bucketized attribute representations is a popular step before sharing a dataset. It is, however, evident that this step can cause significant bias in data and amplify unfairness in downstream tasks. In this paper, we address this issue by introducing the unbiased binning problem that, given an attribute to bucketize, finds its closest discretization to equal-size binning that satisfies group parity across different buckets. Defining a small set of boundary candidates, we prove that unbiased binning must se
The increasing scrutiny on AI ethics and fairness, particularly in data processing, makes research into unbiased methods for attribute representation critically relevant.
Ensuring fairness in data preprocessing steps, like binning, is crucial for mitigating algorithmic bias and preventing the amplification of unfairness in AI systems impacting various societal domains.
This research introduces a novel approach to data discretization that explicitly prioritizes group parity, offering a method to create fairer attribute representations before data is used for downstream tasks.
- · AI developers
- · Ethical AI advocates
- · Data scientists
- · Regulators
- · Organizations relying on biased data models
- · Traditional binning methods
Improved fairness metrics in AI models trained on pre-processed data using unbiased binning.
Increased adoption of fairness-aware data preprocessing techniques across industries.
Reduced legal and reputational risks for companies due to more equitable outcomes from their AI applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI