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A wide variety of fairness metrics and eXplainable Artificial Intelligence (XAI) approaches have been proposed in the literature to identify bias in machine learning models that are used in critical real-life contexts.
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Certifying and removing disparate impact
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Unequal representation and gender stereotypes in image search results for occupations
Matthew Kay, Cynthia Matuszek, and Sean A. Munson · 2015
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Optimized pre-processing for discrimination prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Dheeru Dua and Casey Graff · 2017
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Avoiding discrimination through causal reasoning
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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Inverse classification for comparison-based interpretability in machine learning
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei Koh, and Percy Liang · 2017
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Fairtest: Discovering unwarranted associations in data-driven applications
Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels, and Huang Lin · 2017
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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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’it’s reducing a human being to a percentage’ perceptions of justice in algorithmic decisions
Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao, and Nigel Shadbolt · 2018
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A guide to the california consumer privacy act of 2018
Lydia de la Torre · 2018
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Potential biases in machine learning algorithms using electronic health record data
Milena A Gianfrancesco, Suzanne Tamang, Jinoos Yazdany, and Gabriela Schmajuk · 2018
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Stronger data poisoning attacks break data sanitization defenses
Pang Wei Koh, Jacob Steinhardt, and Percy Liang · 2018
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Fair, transparent, and accountable algorithmic decision-making processes
Bruno Lepri, Nuria Oliver, Emmanuel Letouzé, Alex Pentland, and Patrick Vinck · 2018
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Consistent individualized feature attribution for tree ensembles
Scott M Lundberg, Gabriel G Erion, and Su-In Lee · 2018
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Fair inference on outcomes
Razieh Nabi and Ilya Shpitser · 2018
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Bias in OLAP queries: Detection, explanation, and removal
Babak Salimi, Johannes Gehrke, and Dan Suciu · 2018
Cited alongside, same era.
A symbolic approach to explaining bayesian network classifiers
Andy Shih, Arthur Choi, and Adnan Darwiche · 2018
Cited alongside, same era.
Fairness definitions explained
Sahil Verma and Julia Rubin · 2018
Cited alongside, same era.
Representer point selection for explaining deep neural networks
Chih-Kuan Yeh, Joon Sik Kim, Ian EH Yen, and Pradeep Ravikumar · 2018
Cited alongside, same era.
Training set debugging using trusted items
Xuezhou Zhang, Xiaojin Zhu, and Stephen Wright · 2018
Cited alongside, same era.
Ai and algorithmic bias: Source, detection, mitigation and implications
Runshan Fu, Yan Huang, and P. Singh · 2020
Later among the works it cites.
A distributional framework for data valuation
Amirata Ghorbani, Michael Kim, and James Zou · 2020
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Towards trustable explainable ai
Alexey Ignatiev · 2020
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Subpopulation data poisoning attacks
Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, and Alina Oprea · 2020
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Subpopulation data poisoning attacks
Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, and Alina Oprea · 2020
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2019
Cited alongside, same era.
Self-driving cars more likely to hit blacks. https://www.technologyreview.com/2019/03/01/136808/self-driving-cars-are-coming-but-accidents-may-not-be-evenly-distributed/ , 2019
2019
Cited alongside, same era.
Kjersti Aas, Martin Jullum, and Anders Løland · 2019
Cited alongside, same era.
Assessing and remedying coverage for a given dataset
Abolfazl Asudeh, Zhongjun Jin, and HV Jagadish · 2019
Cited alongside, same era.
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2019
Cited alongside, same era.
Path-specific counterfactual fairness
Silvia Chiappa · 2019
Cited alongside, same era.
Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability
Christopher Frye, Ilya Feige, and Colin Rowat · 2019
Cited alongside, same era.
Ninareh Mehrabi, Muhammad Naveed, Fred Morstatter, and Aram Galstyan · 2020
Later among the works it cites.
Interpretable Machine Learning
Christoph Molnar · 2020
Later among the works it cites.
Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan · 2020
Later among the works it cites.
Robust fairness under covariate shift
Ashkan Rezaei, Anqi Liu, Omid Memarrast, and Brian Ziebart · 2020
Later among the works it cites.
Poisoning attacks on algorithmic fairness
David Solans, Battista Biggio, and Carlos Castillo · 2020
Later among the works it cites.
Responsible data management
Julia Stoyanovich, Bill Howe, and H. V. Jagadish · 2020
Later among the works it cites.
Counterfactual explanations for machine learning: A review
Sahil Verma, John Dickerson, and Keegan Hines · 2020
Later among the works it cites.
Complaint-driven training data debugging for query 2.0
Weiyuan Wu, Lampros Flokas, Eugene Wu, and Jiannan Wang · 2020
Later among the works it cites.
Priu: A provenance-based approach for incrementally updating regression models
Yinjun Wu, V. Tannen, and S. Davidson · 2020
Later among the works it cites.
Fairness-aware instrumentation of preprocessing pipelines for machine learning
K. Yang, Biao Huang, Julia Stoyanovich, and Sebastian Schelter · 2020
Later among the works it cites.
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NYPD stop, question and frisk data. https://www1.nyc.gov/site/nypd/stats/reports-analysis/stopfrisk.page · 2021
Closest in time.
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Stop-and-frisk in the de blasio era. https://www.nyclu.org/en/publications/stop-and-frisk-de-blasio-era-2019 · 2021
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Identifying insufficient data coverage for ordinal continuous-valued attributes
Abolfazl Asudeh, Nima Shahbazi, Zhongjun Jin, and HV Jagadish · 2021
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Missing the missing values: The ugly duckling of fairness in machine learning
Martínez-Plumed Fernando, Ferri Cèsar, Nieves David, and Hernández-Orallo José · 2021
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Explaining black-box algorithms using probabilistic contrastive counterfactuals
Sainyam Galhotra, Romila Pradhan, and Babak Salimi · 2021
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The importance of modeling data missingness in algorithmic fairness: A causal perspective
Naman Goel, Alfonso Amayuelas, Amit Deshpande, and Amit Sharma · 2021
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Mlinspect: A data distribution debugger for machine learning pipelines
Stefan Grafberger, Shubha Guha, Julia Stoyanovich, and Sebastian Schelter · 2021
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Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
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Approximate data deletion from machine learning models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 2021
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Measurement and fairness
Abigail Z Jacobs and Hanna Wallach · 2021
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Reasons, values, stakeholders: A philosophical framework for explainable artificial intelligence
Atoosa Kasirzadeh · 2021
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Efficient computation and analysis of distributional shapley values
Yongchan Kwon, Manuel A. Rivas, and James Zou · 2021
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Exacerbating algorithmic bias through fairness attacks
Ninareh Mehrabi, Muhammad Naveed, Fred Morstatter, and Aram Galstyan · 2021
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Sliceline: Fast, linear-algebra-based slice finding for ml model debugging
Svetlana Sagadeeva and Matthias Boehm · 2021
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Hedgecut: Maintaining randomised trees for low-latency machine unlearning
Sebastian Schelter, Stefan Grafberger, and Ted Dunning · 2021
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Fairness violations and mitigation under covariate shift
Harvineet Singh, Rina Singh, Vishwali Mhasawade, and Rumi Chunara · 2021
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Fair classification with group-dependent label noise
Jialu Wang, Yang Liu, and Caleb Levy · 2021
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