Research map / Learning theory and algorithms

Fairness: research map

516 accepted papers on Fairness in Learning theory and algorithms, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 7 approaches. The busiest year so far is 2024.

Within Learning theory and algorithms, its share shrank from 7.5% in 2023–24 to 5.8% in 2025–26 (167 → 140 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).

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Approaches and key papers

fair · classifier · classification · 356 papers

Approaches in this cluster:

Fairness-constrained classification (148 papers)
Learn classifiers under group or individual fairness constraints, with guarantees on fairness and accuracy.
Post-processing and transport for fairness (101 papers)
Adjust or transform classifiers, often via optimal transport, to remove discrimination while preserving utility.
Fair allocation and selection (69 papers)
Design fair rankings, resource allocation and subset selection algorithms with provable fairness guarantees.
Minimax fairness via sampling (38 papers)
Reach group fairness by reweighting or actively sampling data, including without demographic labels.

Most cited and most cited since 2024:

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness (ICML 2018 · 383 citations)
On Fairness and Calibration (NeurIPS 2017 · 279 citations)
Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification (AAAI 2024 · 18 citations)
A Sequentially Fair Mechanism for Multiple Sensitive Attributes (AAAI 2024 · 9 citations)

decision · making · systems · 160 papers