Discrimination- and privacy-aware patterns

By Sara Hajian, Josep Domingo-Ferrer, Anna Monreale, Dino Pedreschi, Fosca Giannotti. 2015

97 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety Research, Sociology and Political Science

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Citations in the corpus

Learning Adversarially Fair and Transferable Representations (2018, background): “Calmon et al. (2017) and Hajian et al. (2015) explored fair machine learning by pre-and post-processing training datasets.”
Stable and Fair Classification (2019, methodology): “Recent studies [35, 36, 42, 43, 64] have expanded the application of methods to achieve both goals; see a recent paper [23] for more discussions.”
Achieving Non-Discrimination in Data Release (2016, background): “In [13], the authors proposed to sanitize discriminatory patterns by incorporating the privacy preserving methods.”
Exposing the probabilistic causal structure of discrimination (2015, background): “In the state of the art of data mining methods for discrimination discovery, it is also known as explainable discrimination [12] and conditional discrimination [45].”
What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness (2020, neutral): “, [33, 46, 51, 52, 81]).”
Privacy for All: Ensuring Fair and Equitable Privacy Protections (2018, methodology): “Hajian et al. (2015) use pattern sanitization methods including k-anonymity and differential privacy to simultaneously achieve privacy and fairness.”
On Discrimination Discovery Using Causal Networks (2016, background): “, association rules for group discrimination discovery [16,17], situation testing for individual discrimination discovery [11], correlation analysis considering explanatory attributes for direct discrimination discovery [6,25], rule inference for indirect discrimination discovery [5,16], and fair cl”
Interpretability of deep learning models: A survey of results (2017, background): “We would need to develop new computational techniques for discrimination-aware data mining [40].”
FAHT: An Adaptive Fairness-aware Decision Tree Classifier (2019, background): “In [Hajian et al. , 2015], the fair patterns are processed with k -anonymity to work against discrimination.”
Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining (2016, methodology): “Fairness-aware data mining: Pre-processing approaches including correcting the training data; inprocessing approaches including näıve bayes models, logistic regression, decision trees, hinge loss, support vector machines, adaptive boosting and classification; post-processing approaches including cla”
Fairness in Machine Learning (2020, methodology): “Pre-Processing methods consist in transforming the training data to remove undesired biases [16, 21, 24, 28, 47, 51, 52, 53, 61, 64, 65, 66, 67, 86, 90, 91, 92, 112, 114, 116, 118, 121, 122, 127, 126, 164, 174, 188, 190, 191, 193].”
Measuring discrimination in algorithmic decision making (2017, methodology): “…a standard model and then modiﬁes this model to obey non-discrimination constraints, for instance, by changing the labels of some leaves in a decision tree (Calders and Verwer 2010; Kamiran et al. 2010), or removing selected rules from the set of discovered decision rules (Hajian et al. 2015).”
Anti-discrimination learning: a causal modeling-based framework (2017, methodology): “Proposed methods for discrimination prevention using algorithm tweaking require some tweak of predictive models [7,9,14,15,18,19,35].”
Situation Testing-Based Discrimination Discovery: A Causal Inference Approach (2016, background): “The di↵erence between p1 and p2, i.e., p1 p2 reflects the bias due to the membership to the protected group.”