A Resource-Aware Taxonomy of AI Bias Mitigation Techniques

Loreti, Daniela; Calegari, Roberta; Milano, Michela

doi:10.24963/ijcai.2026/882

A Resource-Aware Taxonomy of AI Bias Mitigation Techniques

Daniela Loreti, Roberta Calegari, Michela Milano

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Survey Track. Pages 7949-7957.

https://doi.org/10.24963/ijcai.2026/882

Keywords

AI Ethics, Trust, Fairnes: Ethical, legal and societal issues
AI Ethics, Trust, Fairnes: Trustworthy AI
AI Ethics, Trust, Fairnes: Fairness and diversity
AI Ethics, Trust, Fairnes: Bias

Abstract

The literature on AI fairness has grown rapidly, proposing a large number of bias mitigation techniques that are commonly organized into pre-, in-, and post-processing methods. This pipeline-centric view offers an operational, lifecycle-based perspective on where mitigation can be applied. In deployment settings, however, practitioners also face an additional question: whether a mitigation family is applicable given the resources and access rights available in a concrete system.

In this survey, we use resources broadly to denote data access/control, training capability, and deployment-time interface/decision control.

Accordingly, we introduce a resource-aware taxonomy that complements existing taxonomies by classifying AI bias mitigation methods according to the conditions that make them practically implementable. We use this taxonomy to structure and reinterpret existing literature on the topic, highlighting which mitigation families remain feasible under resource constraints.

Daniela Loreti, Roberta Calegari, and Michela Milano. 2026. A Resource-Aware Taxonomy of AI Bias Mitigation Techniques. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 7949–7957. https://doi.org/10.24963/ijcai.2026/882

@inproceedings{ijcai2026p882,
  title     = {A Resource-Aware Taxonomy of AI Bias Mitigation Techniques},
  author    = {Loreti, Daniela and Calegari, Roberta and Milano, Michela},
  booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-26}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Diego Calvanese},
  pages     = {7949--7957},
  year      = {2026},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2026/882},
  url       = {https://doi.org/10.24963/ijcai.2026/882},
}

TY  - CPAPER
TI  - A Resource-Aware Taxonomy of AI Bias Mitigation Techniques
AU  - Loreti, Daniela
AU  - Calegari, Roberta
AU  - Milano, Michela
T2  - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2026
SP  - 7949
EP  - 7957
DO  - 10.24963/ijcai.2026/882
UR  - https://doi.org/10.24963/ijcai.2026/882
ER  -