Chris Russell

Artificial Intelligence · with Kusner · 2017–2020

A researcher in the Science corpus: 8 papers; trusts 83 researchers and is trusted by 101 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #2,611 of 35,345 by trust here, #3,816 by citations
• Safety, Robustness & Interpretability — 85 citations into it; #121 of 2,251 by trust here, #152 by citations
• Safety Research — 41 citations into it
• AI Ethics, Fairness & Society — 38 citations into it; #41 of 494 by trust here, #25 by citations
• Sociology and Political Science — 34 citations into it
• Causal Inference & Discovery — 29 citations into it; #71 of 364 by trust here, #35 by citations
• Statistics and Probability — 25 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it

Whom Chris Russell trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. David Martens
2. Judea Pearl
3. Sandra Wachter
4. R. Caruana
5. J. Gerring
6. Foster J. Provost
7. Brent Daniel Mittelstadt
8. Geoffrey E. Hinton
9. Tim Miller
10. Ilya Shpitser

Who trusts Chris Russell overall

1. Amir-Hossein Karimi
2. Shubham Sharma
3. Silvia Chiappa
4. Ilia Stepin
5. Susanne Dandl
6. Ana Lucic
7. Roberto Confalonieri
8. Divyat Mahajan
9. Sahil Verma
10. Dana Pessach

Papers from Chris Russell's perspective

Ordered by how strongly the researchers Chris Russell trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR (2017) — 3,169 citations
• Explaining Explanations in AI (2018) — 780 citations
• Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI (2020) — 404 citations
• Efficient Search for Diverse Coherent Explanations (2019) — 278 citations
• Making Decisions that Reduce Discriminatory Impacts (2019) — 29 citations
• Causal Reasoning for Algorithmic Fairness (2018) — 147 citations
• Counterfactual Fairness (2017) — 1,918 citations
• When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness (2017) — 180 citations