Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems A Datta, S Sen, Y Zick 2016 IEEE symposium on security and privacy (SP), 598-617, 2016 | 962 | 2016 |
Machine learning explainability in finance: an application to default risk analysis P Bracke, A Datta, C Jung, S Sen Bank of England Working Paper, 2019 | 154 | 2019 |
Measurement of prompt J/ψ pair production in pp collisions at = 7 Tev V Khachatryan, AM Sirunyan, A Tumasyan, W Adam, T Bergauer, ... Journal of High Energy Physics 2014 (9), 1-35, 2014 | 142 | 2014 |
Bootstrapping privacy compliance in big data systems S Sen, S Guha, A Datta, SK Rajamani, J Tsai, JM Wing 2014 IEEE Symposium on Security and Privacy, 327-342, 2014 | 122 | 2014 |
Influence-directed explanations for deep convolutional networks K Leino, S Sen, A Datta, M Fredrikson, L Li 2018 IEEE international test conference (ITC), 1-8, 2018 | 83 | 2018 |
Proxy non-discrimination in data-driven systems A Datta, M Fredrikson, G Ko, P Mardziel, S Sen arXiv preprint arXiv:1707.08120, 2017 | 71 | 2017 |
Debugging machine learning tasks A Chakarov, A Nori, S Rajamani, S Sen, D Vijaykeerthy arXiv preprint arXiv:1603.07292, 2016 | 58 | 2016 |
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs A Datta, M Fredrikson, G Ko, P Mardziel, S Sen Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications …, 2017 | 54 | 2017 |
SoK: Differential privacy as a causal property MC Tschantz, S Sen, A Datta 2020 IEEE Symposium on Security and Privacy (SP), 354-371, 2020 | 53 | 2020 |
Feature-wise bias amplification K Leino, E Black, M Fredrikson, S Sen, A Datta arXiv preprint arXiv:1812.08999, 2018 | 50 | 2018 |
A logic of programs with interface-confined code L Jia, S Sen, D Garg, A Datta 2015 IEEE 28th Computer Security Foundations Symposium, 512-525, 2015 | 23 | 2015 |
System and method for assisting in the provision of algorithmic transparency A Datta, S Sen, Y Zick US Patent App. 15/796,222, 2018 | 17 | 2018 |
Differential privacy as a causal property MC Tschantz, S Sen, A Datta arXiv preprint arXiv:1710.05899, 2017 | 12 | 2017 |
Machine learning explainability and robustness: connected at the hip A Datta, M Fredrikson, K Leino, K Lu, S Sen, Z Wang Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 9 | 2021 |
Supervising feature influence S Sen, P Mardziel, A Datta, M Fredrikson arXiv preprint arXiv:1803.10815, 2018 | 6 | 2018 |
Latent factor interpretations for collaborative filtering A Datta, S Kovaleva, P Mardziel, S Sen arXiv preprint arXiv:1711.10816, 2017 | 5 | 2017 |
Correspondences between privacy and nondiscrimination: why they should be studied together A Datta, S Sen, MC Tschantz arXiv preprint arXiv:1808.01735, 2018 | 3 | 2018 |
Use Privacy in Data-Driven Systems A Datta, M Fredrikson, G Ko, P Mardziel, S Sen Proceedings of the ACM Conference on Computer and Communications Security, 2017 | 3 | 2017 |
System and method for explaining the behavior of neural networks K Leino, S Sen, A Datta, M Fredrikson US Patent App. 16/583,392, 2021 | 2 | 2021 |
Staff Working Paper No. 816 Machine learning explainability in finance: an application to default risk analysis P Bracke, A Datta, C Jung, S Sen Technical report, Bank of England, 2019 | 2 | 2019 |