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AI algorithms are not immune to biases.
Visualizing Data using t-SNE
Laurens Van Der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Human model evaluation in interactive supervised learning
Rebecca Fiebrink, Perry R. Cook, and Dan Trueman. 2011 · 2011
Earlier work this paper cites.
Power to the People: The Role of Humans in Interactive Machine Learning
Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014 · 2014
Earlier work this paper cites.
Debiasing Word Embedding
Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016 · 2016
Earlier work this paper cites.
European Union regulations on algorithmic decision-making and a "right to explanation"
Bryce Goodman and Seth Flaxman. 2016 · 2016
Earlier work this paper cites.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
UX Design Innovation: Challenges for Working with Machine Learning as a Design Material
Graham Dove, Kim Halskov, Jodi Forlizzi, and John Zimmerman. 2017 · 2017
Earlier work this paper cites.
Addressing bias in machine learning algorithms: A pilot study on emotion recognition for intelligent systems
Ayanna Howard, Cha Zhang, and Eric Horvitz. 2017 · 2017
Earlier work this paper cites.
Counterfactual fairness
Matt Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
Earlier work this paper cites.
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation
Sarah Tan, Rich Caruana, Giles Hooker, and Yin Lou. 2017 · 2017
Earlier work this paper cites.
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017 · 2017
Earlier work this paper cites.
The Role of Design in Creating Machine-Learning-Enhanced User Experience
Qian Yang. 2017 · 2017
Earlier work this paper cites.
Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai Wei Chang. 2017 · 2017
Cited alongside, same era.
Trends and Trajectories for Explainable, Accountable and Intelligible Systems
Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y. Lim, and Mohan Kankanhalli. 2018 · 2018
Cited alongside, same era.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi and Mohammed Berrada. 2018 · 2018
Cited alongside, same era.
The accuracy, fairness, and limits of predicting recidivism
Julia Dressel and Hany Farid. 2018 · 2018
Cited alongside, same era.
Word embeddings quantify 100 years of gender and ethnic stereotypes
Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou. 2018 · 2018
Cited alongside, same era.
Learning Not to Learn: Training Deep Neural Networks with Biased Data
Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings
Mohsan Alvi, Andrew Zisserman, and Christoffer Nellåker. 2019 · 2019
Later among the works it cites.
Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure
Alexander Amini, Ava Soleimany, Wilko Schwarting, Sangeeta Bhatia, and Daniela Rus. 2019 · 2019
Later among the works it cites.
The effects of example-based explanations in a machine learning interface. In Proceedings of the 24th International Conference on Intelligent User Interfaces - IUI ’19
Carrie J Cai, Jonas Jongejan, and Jess Holbrook. 2019 · 2019
Later among the works it cites.
Exploring Neural Networks with Activation Atlases
Shan Carter, Zan Armstrong, Ludwig Schubert, Ian Johnson, and Chris Olah. 2019 · 2019
Later among the works it cites.
Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders
Hao-Fei Cheng, Ruotong Wang, Zheng Zhang, Fiona O’Connell, Terrance Gray, F. Maxwell Harper, and Haiyi Zhu. 2019 · 2019
Later among the works it cites.
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Byungju Kim, Hyunwoo Kim, Kyungsu Kim, Sungjin Kim, and Junmo Kim. 2018 · 2018
Cited alongside, same era.
Cluster-Based Visual Abstraction for Multivariate Scatterplots
Hongsen Liao, Yingcai Wu, Li Chen, and Wei Chen. 2018 · 2018
Cited alongside, same era.
ScatterNet: A Deep Subjective Similarity Model for Visual Analysis of Scatterplots
Yuxin Ma, Anthony K.H. Tung, Wei Wang, Xiang Gao, Zhigeng Pan, and Wei Chen. 2018 · 2018
Cited alongside, same era.
The Building Blocks of Interpretability
Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, and Alexander Mordvintsev. 2018 · 2018
Cited alongside, same era.
Machine Learning as a UX Design Material: How Can We Imagine Beyond Automation, Recommenders, and Reminders?
Qian Yang. 2018 · 2018
Cited alongside, same era.
Grounding Interactive Machine Learning Tool Design in How Non-Experts Actually Build Models
Qian Yang, Jina Suh, Nan-Chen Chen, and Gonzalo Ramos. 2018 · 2018
Cited alongside, same era.
Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation
Jichen Zhu, Antonios Liapis, Sebastian Risi, Rafael Bidarra, and G. Michael Youngblood. 2018 · 2018
Cited alongside, same era.
Mitigating bias in gender, age and ethnicity classification: A multi-task convolution neural network approach
Abhijit Das, Antitza Dantcheva, and Francois Bremond. 2019 · 2019
Later among the works it cites.
Can Children Understand Machine Learning Concepts? The Effect of Uncovering Black Boxes
Tom Hitron, Yoav Orlev, Iddo Wald, Ariel Shamir, Hadas Erel, and Oren Zuckerman. 2019 · 2019
Later among the works it cites.
Racial bias in a medical algorithm favors white patients over sicker black patients
CY Johnson. 2019 · 2019
Later among the works it cites.
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Yuxin Ma, Tiankai Xie, Jundong Li, and Ross Maciejewski. 2019 · 2019
Later among the works it cites.
Can we do better explanations? A proposal of user-centered explainable AI
Mireia Ribera and Agata Lapedriza. 2019 · 2019
Later among the works it cites.
Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety.. In SafeAI@ AAAI
Kacper Sokol and Peter A Flach. 2019 · 2019
Later among the works it cites.
Interactive Visualizer to Facilitate Game Designers in Understanding Machine Learning. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems - CHI EA ’19
Jiachi Xie, Chelsea M. Myers, and Jichen Zhu. 2019 · 2019
Later among the works it cites.