CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models
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[1] Rachel K. E. Bellamy,et al. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias , 2018, ArXiv.
[2] Zhou Wang,et al. Why is image quality assessment so difficult? , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[3] Ajmal Mian,et al. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey , 2018, IEEE Access.
[4] Zhou Wang,et al. Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.
[5] Chris Russell,et al. Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR , 2017, ArXiv.
[6] Jinfeng Yi,et al. Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach , 2018, ICLR.
[7] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Yang Liu,et al. Actionable Recourse in Linear Classification , 2018, FAT.
[9] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[10] Franco Turini,et al. A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..
[11] Chris Russell,et al. Efficient Search for Diverse Coherent Explanations , 2019, FAT.
[12] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[13] Alexander Binder,et al. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.
[14] Reuben Binns,et al. Fairness in Machine Learning: Lessons from Political Philosophy , 2017, FAT.
[15] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[16] Franco Turini,et al. Meaningful Explanations of Black Box AI Decision Systems , 2019, AAAI.
[17] Franco Turini,et al. Local Rule-Based Explanations of Black Box Decision Systems , 2018, ArXiv.