Classifying without discriminating

By Faisal Kamiran, Toon Calders. 2009

505 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety, Robustness & Interpretability

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Citations in the corpus

Exposing and Correcting the Gender Bias in Image Captioning Datasets and Models (2019, background): “Some works [Calders et al., 2009], [Kamiran and Calders, 2009] modify/re-weight the class labels to reduce the inﬂuence of biased data.”
Does Object Recognition Work for Everyone? (2019, background): “For instance, statistical parity [5, 6, 14, 23–25, 30, 49] states that in binary-classification settings, members of different groups should have the same chance of receiving a positive prediction.”
Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy (2019, background): “Algorithmic solutions have ranged from removing undesired bias by preprocessing the data [39, 59], striking a tradeoff between performance and fairness by posing additional regularization during training or inference [34, 40, 52, 80, 81, 83], or designing application-specific interventions (such as ”
Certifying and Removing Disparate Impact (2014, methodology): “[8] F. Kamiran and T. Calders.”
Inherent Trade-Offs in the Fair Determination of Risk Scores (2016, background): “Kamiran and Calders [21] and Hajian and Domingo-Ferrer [18]seek to modify datasets to remove any information that might permit discrimination.”
A Survey on Bias and Fairness in Machine Learning (2019, background): “Other pre-processing approaches, such as messaging [74], preferential sampling [75, 76], disparate impact removal [51], also aim to remove biases from the data.”
Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, methodology): “[65, 11, 36, 61] give algorithms to build models achieving this notion of fairness”
Data preprocessing techniques for classification without discrimination (2011, methodology): “Experiments over the rather small German Credit dataset available in the UCI ML repository have not been included here but can be found in [13].”
Wasserstein Fair Classification (2019, methodology): “…that do not contain sensitive information during training (Beutel et al., 2017; Calders et al., 2009; Calmon et al., 2017; Edwards and Storkey, 2016; Feldman et al., 2015; Fish et al., 2015; Kamiran and Calders, 2009, 2012; Louizos et al., 2016; Zemel et al., 2013; ˇZliobaite et al., 2011).”
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (2019, methodology): “First, pre-processing approaches, which rely on modifying the data to eliminate or neutralize any preexisting bias and subsequently apply standard ML techniques (Kamiran and Calders 2012; 2009; Luong, Ruggieri, and Turini 2011).”
Stable and Fair Classification (2019, methodology): “Finally, a diﬀerent line of research pre-processes the training data with the goal of removing the bias for learning, e.g., [39, 51, 40, 76, 26, 44].”
Fairness and Abstraction in Sociotechnical Systems (2019, background): “Many papers have beenwritten proposing definitions of fairness, and then based on those, generating best approximations or fairness guarantees based on hard constraints or fairness metrics [24, 32, 39, 40, 72].”
Taking Advantage of Multitask Learning for Fair Classification (2018, background): “This is in contrast to other approaches which impose the fairness constraint as a post-processing step [10 19 20 31] or by modifying the data representation before employing standard machine learning methods [1 12 22–24 39].”
FairGAN: Fairness-aware Generative Adversarial Networks (2018, methodology): “The fundamental assumption of the preprocess methods is that, once a classifier is trained on a discrimination-free dataset, the prediction made by the classifier will also be discrimination free [5].”
Causal Reasoning for Algorithmic Fairness (2018, methodology): “Demographic parity has been used, for several purposes, in the following works: [14, 19, 20, 24, 42, 43].”
Invariant Representations without Adversarial Training (2018, background): “More recently the algorithmic fairness community has investigated fair methods [12] and fair representations [22].”
Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification (2018, background): “Hence, it has often been argued that we should look for ways to remove bias from training data (instead of constraining the training process), either through massaging the training labels [24] or reweighting training samples according to an estimated probability that they belong to a sensitive group”