Fairness-Aware Classifier with Prejudice Remover Regularizer

By Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma. 2012

896 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety Research, Sociology and Political Science, Safety, Robustness & Interpretability

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Citations in the corpus

Does Gender Matter? Towards Fairness in Dialogue Systems (2019, background): “Existing works attempt to address the issue of fairness in various machine learning tasks such as classification (Kamishima et al., 2012; Zafar et al., 2015), regression (Berk et al.”
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods (2018, background): “For binary classiﬁcation Kamishima et al. (2012, 2011) add a regularization term to their objective that penalizes biased predictions.”
A Survey on Bias and Fairness in Machine Learning (2019, methodology): “Therefore, certain methods have been proposed [49, 65, 72, 86] that satisfy certain definitions of fairness.”
Learning Adversarially Fair and Transferable Representations (2018, methodology): “It is common to instead optimize a relaxed objective (Kamishima et al., 2012), whose hyperparameter values negotiate a tradeoff between maximizing utility (usually classification accuracy) and fairness.”
On formalizing fairness in prediction with machine learning (2017, background): “A number of proposed predictors in the machine learning literature satisfy this measure (15; 29), while some don’t (7; 21; 25).”
Attributing Fair Decisions with Attention Interventions (2021, background): “…Moyer et al. 2018; Roy and Boddeti 2019; Jaiswal et al. 2020; Song et al. 2019), adding regularizers to incorporate fairness (Zafar et al. 2017; Kamishima et al. 2012; Mehrabi, Huang, and Morstatter 2020), or modifying the outcomes of unfair machine learning algorithms to ensure fairness…”
Statistical Equity: A Fairness Classification Objective (2020, methodology): “Some other methods perform in-processing which tries to incorporate the fairness objective during training phase [Kamishima et al., 2012, Zafar et al., 2015, Wu, Zhang, and Wu, 2018].”
Fair Regression: Quantitative Definitions and Reduction-based Algorithms (2019, background): “Kamishima et al. (2012) and Fukuchi et al. (2013) seek to ﬁt a probabilistic model that satisﬁes statistical independence, but they do not present efﬁcient algorithms or statistical guarantees.”
Compositional Fairness Constraints for Graph Embeddings (2019, background): “While enforcing invariance constraints on general classiﬁcation models (Chouldechova, 2017; Gajane & Pechenizkiy, 2017; Kamishima et al., 2012) and collaborative ﬁltering algorithms (Yao & Huang, 2017) has received considerable attention in recent years, these techniques have yet to be considered…”
Stable and Fair Classification (2019, methodology): “They observed that the standard deviation of the fairness metric is 2.4 % for the algorithm in [41] ( KAAS ) with respect to the race attribute, and is 4.1 % for that in [75] ( ZVRG ) with respect to the sex attribute.”
Fairness and Abstraction in Sociotechnical Systems (2019, background): “Many papers have been written 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].”
Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved (2018, neutral): “Therefore, z 1 neighborhoods z , z , z 3”
The Price of Fair PCA: One Extra Dimension (2018, background): “…attribute directly [Pedreshi et al., 2008; Kamiran et al., 2010; Calders and Verwer, 2010; Kamiran and Calders, 2011; Luong et al., 2011; Kamiran et al., 2012; Kamishima et al., 2012; Hajian and Domingo-Ferrer, 2013; Feldman et al., 2015; Zafar et al., 2015; Fish et al., 2016; Adler et al., 2016].”
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias (2018, methodology): “, 2015), prejudice remover (Kamishima et al., 2012), and two-Naive Bayes (Calders & Verwer, 2010).”
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].”
Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification (2018, background): “None 27% 85% 31% 91% ASR+CULEP acc + pRule 100% 82% 99% 89% Covariance [44] 94% 82% 83% 91% Regularizer [28] 85% 83% 100% 91%”
A Convex Framework for Fair Regression (2017, methodology): “…of data (e.g. [18]); inprocessing, during the training of a model as either a constraint or incorporated into the objective function (e.g. [11, 21, 33]); or postprocessing, where data is labeled by some black-box model and then relabeled as a function only of the original labels (e.g. [14]).”