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We discuss techniques such as adversarial debiasing and fairness constraints to achieve this. We also delve into ethical considerations, regulatory implications, and best practices to ensure …
Feb 14, 2025 · This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics to ensure that predictions are not influenced by biased …
Oct 3, 2024 · This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics to ensure that predictions are not influenced by biased variables.
If a machine learning model acquires unintentional biases, it may be unable to capture the true relationship between the features and the target outcome.
Oct 26, 2023 · To address this issue, an in-processing approach, Adversarial Debiasing, has been proposed. Adversarial Debiasing aims to mitigate both dataset biases and implicit biases …
Christina Wadsworth, Francesca Vera, and Chris Piech. Achieving fairness through adversarial learning: an application to recidivism prediction. arXiv preprint arXiv:1807.00199, 2018.
As a result, a new field of research on algorithmic fairness has emerged. In this area, detecting biases and mitigating them is essential to ensure fair and discrimination-free decisions.
This paper proposes an end-to-end learnable framework for fairness-aware network pruning, which optimizes both pruning and debias tasks jointly by adversarial training against those …
Oct 27, 2023 · To enforce this objective, we introduce ROAD, a novel approach that leverages the Distributionally Robust Optimization (DRO) framework within a fair adversarial learning …
This paper proposes an end-to-end learnable framework for fairness-aware network pruning, which optimizes both pruning and debias tasks jointly by adversarial training against those …
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