Literatur KI-Absicherung

1. Verlässlichkeit
Bastani, O., Ioannou, Y., Lampropoulos, L., Vytiniotis, D.,  Nori, A. & Criminisi, A. (2016): Measuring Neural Net Robustness with Constraints. In: Advances in neural information processing systems. pp. 2613-2621.
Dreossi, T.,  Ghosh, S.,  Sangiovanni-Vincentelli, A. & Seshia, S.-A. (2017): Systematic Testing of Convolutional Neural Networks for Autonomous Driving. In: Reliable Machine Learning in the Wild (RMLW).
Gal, K. und Y. (2017): What Uncertainties Do We Need in Bayesian Deep Learning For Computer Vision? Neural Information Processing Systems.
Gal, Y. & Zoubin, G. (2016): Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. International Conference on Machine Learning (ICML).
Hein, M. & Andriushchenko, M. (2017): Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. In: Advances in Neural Information Processing Systems (NIPS).
Kuper, L., Katz, G., Gottschlich, J.,  Julian, K., Barrett, C. & Kochenderfer, M. (2018): Toward Scalable Verification for Safety-Critical Deep Networks. In: SysML.
Lee, K., Lee, H., Lee, K., & Shin, J. (2018): Training confidence-calibrated classifiers for detecting out-of-distribution samples. International Conference on Learning Representations (ICLR).
Liang, S., Li, Y. & Srikant, R. (2018): Enhancing the reliability of out-of-distribution image detection in neural networks. International Conference on Learning Representations (ICLR).
Mirman, M., Gehr, T. & Veche, M. (2018): Differentiable Abstract Interpretation for Provably Robust Neural Networks. In: International Conference on Machine Learning.
Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, D. L., S. S. A., A., Müller E. &  Kloft, M. (2018): Deep One-Class Classification. International Conference on Machine Learning (ICML).
Weng, T.-W., Zhang, H., Chen, P.-Y., Yi, J., Su, D., Gao, Y., Hsieh, C.-J. & Daniel, L. (2018): Evaluating the Robustness of Neural Networks - An Extreme Value Theory Approach. In: International Conference on Learning Representations (ICLR).
2. FAIRness
Berk, R.,  Heidari, H., Jabbari, S., Kearns, M. & Roth, A. (2017): Fairness in Criminal Justice Risk Assessments: The State of the Art. Sociological Methods & Research.
Kilbertus, N., Carulla, M. R., Parascandolo, G., Hardt, M., Janzing, D., & Schölkopf, B. (2017): Avoiding discrimination through causal reasoning. In: Advances in Neural Information Processing Systems (pp. 656-666).
Liu, L. T., Dean, S., Rolf, E., Simchowitz, M. & Hardt, M. (2018): Delayed impact of fair machine learning. Proceedings of the 35th International Conference on Machine Learning (ICML).
Udeshi, S., Arora, P. & Chattopadhyay, S. (2018): Automated Directed Fairness Testing. In: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE). Montpellier. pp. 98–108.
3. Erklärbarkeit
Chen, J., Song, L., Wainwright, M. J. & Jordan, M. I. (2018): Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. In: International Conference on Machine Learning (ICML).
Hu, Z., Ma, X., Liu, Z., Hovy, E., & Xing, E. (2016): Harnessing deep neural networks with logic rules. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL). Berlin. Volume 1: Long Papers.
Kapoor, A., Lee, B., Tan, D., & Horvitz, E. (2010): Interactive optimization for steering machine classification. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (ACM) pp. 1343-1352.
Kim, B., Wattenberg M., Gilmer J., Cai, C., Wexler, J. & Viegas, F.(2018): Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) International Conference on Maching Learning (ICML) pp. 2673-2682.
Kim, J. & Canny, J. (2017): Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention. In: International Conference on Computer Vision (ICCV). IEEE.
Koh, P. & Liang, P. (2017): Understanding Black-box Predictions via Influence Functions. In: International Conference on Machine Learning (ICML).
Lundberg, S. & Lee, S. (2017): A Unified Approach to Interpreting Model Predictions. In: Advances in Neural Information Processing Systems. pp. 4768–4777.
Montavon, G. , Lapuschkin, S. , Binder, A., Samek, W. & Müller, K. (2017): Explaining nonlinear classification decisions with deep taylor decomposition. In Pattern Recognition; 65. pp. 221-222
Nagamine, T. & Mesgarani, N. (2017): Understanding the Representation and Computation of Multilayer Perceptrons: A Case Study in Speech Recognition. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70 (ICML).
Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., & Mordvintsev, A. (2018): The building blocks of interpretability. Distill, 3(3), e10.
Pezzotti, N., Höllt, T., Van Gemert, J., Lelieveldt, B. P., Eisemann, E., & Vilanova, A. (2018): Deepeyes: Progressive visual analytics for designing deep neural networks. IEEE transactions on visualization and computer graphics. 24(1), 98-108.
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017): Grad-cam: Visual explanations from deep networks via gradient-based localization. In 2017 IEEE International Conference on Computer Vision (ICCV) pp. 618-626
Zintgraf, L., Cohen, T. & Welling, M. (2016): A new method to visualize deep neural networks. CoRR, abs/1603.02518
4. Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016): Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. pp. 308-318.
Agarwal, N., Suresh, A. T., Xinnan Yu, F.,  Yu, Kumar, S. & McMahan. B. (2018): cpSGD: Communication-efficient and differentially-private distributed SGD. In: Advances in Neural Information Processing Systems.
Kamp, M., Adilova, L., Sicking, J., Hüger, F., Schlicht, P., Wirtz, T., & Wrobel, S. (2018): Efficient decentralized deep learning by dynamic model averaging. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 393-409. Springer, Cham.
Kilbertus N., Gascon A., Kusner M., Veale M., Gummadi K. & Weller A. (2018): Blind justice: fairness with encrypted sensitive attributes. In: Proc. of the 35th Int. Conf. on Machine Learning (ICML), Stockholm. International Machine Learning Society.
Oh, S.-J., Fritz, M. & Schiele, B. (2017): Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective. In: Proceedings of the IEEE International Conference on Computer Vision.
Papernot, N., McDaniel, P.,  Sinha, A. & Wellman, M. (2018): SoK: Security and Privacy in Machine Learning. In: 2018 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE
Shokri, R. & Shmatikov, V. (2015): Privacy-Preserving Deep Learning. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. ACM.
Tramèr, F.,  Zhang, F.,  Juels, A.,  Reiter, M.-K. & Ristenpart, T. (2016): Stealing machine learning models via prediction APIs. In: 25th USENIX Security Symposium (USENIX Security 16) USENIX Association. Austin. TX. Pp. 601–618.
5. Sicherheit
Eykholt, K., Evtimov, I.,  Fernandes, E.,  Li, B.,  Rahmati, A.,  Xiao, C.,  Prakash, A., Kohno, T. & Song, D. (2018): Robust Physical-World Attacks on Deep Learning Visual Classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
Gehr, T.,  Mirman, M.,  Drachsler-Cohen, D., Tsankov, P.,  Chaudhuri, S. & Vechev, M. (2018): 2018 IEEE Symposium on Security and Privacy (SP). IEEE.
Madry, A., Makelov, A., Schmidt, L. , Tsipras, D. & Vladu, A. (2018): Towards Deep Learning Models Resistant to Adversarial Attacks. International Conference on Learning Representations (ICLR).
Nguyen, A. , Yosinski, J. & Clune, J. (2015): Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Computer Vision and Pattern Recognition (CVPR). IEEE.
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B. & Swami, A. (2017): Practical black-box attacks against machine learning. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. pages 506–519.
Salay, R.,  Queiroz, R. & Czarnecki, K. (2017): An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software. In: CX World Congress Experience. SAE International.
Steinhardt, J.,  Wei Koh, P. & Liang, P. (2017): Certified Defenses for Data Poisoning Attacks. In: Advances in Neural Information Processing Systems (NIPS).
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. J. & Fergus, R. (2014): Intriguing properties of neural networks. International Conference on Learning Representations (ICLR) abs/1312.6199
6. Autonomie
Koopman, P. & Wagner, M. (2018): Toward a Framework for Highly Automated Vehicle Safety Validation. In: SAE International Journal of Transportation Safety 4.
Schwarting, W., Alonso-Mora, J. & Rus, D. (2018): Planning and Decision-Making for Autonomous Vehicles. In: Annual Review of Control, Robotics and Autonomous Systems.