Sandra Wachter

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 20 researchers and is trusted by 83 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #3,881 of 35,345 by trust here, #3,783 by citations
• Safety, Robustness & Interpretability — 39 citations into it; #349 of 2,251 by trust here, #359 by citations
• AI Ethics, Fairness & Society — 29 citations into it; #22 of 494 by trust here, #6 by citations
• Safety Research — 26 citations into it; #592 of 5,829 by trust here, #242 by citations
• Information Systems — 16 citations into it
• Sociology and Political Science — 15 citations into it; #2,728 of 22,756 by trust here, #2,752 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Philosophy — 5 citations into it

Whom Sandra Wachter trusts overall

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

1. Jenna Burrell
2. Denis J. Hilton
3. Alan Bundy
4. Avanti Shrikumar
5. Karen Simonyan
6. Luciano Floridi
7. David Martens
8. Brent Daniel Mittelstadt
9. Christian Sandvig
10. Been Kim

Who trusts Sandra Wachter overall

1. Heike Felzmann
2. Q. Liao
3. Atoosa Kasirzadeh
4. Chris Russell
5. Shubham Sharma
6. Kacper Sokol
7. Eva Thelisson
8. Cynthia Rudin
9. Emilio Ferrara
10. Bradley C. Boehmke

Papers from Sandra Wachter's perspective

Ordered by how strongly the researchers Sandra Wachter 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. ImageNet Large Scale Visual Recognition Challenge
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

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 a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation (2017) — 1,085 citations
• A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI (2018) — 434 citations
• Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI (2020) — 404 citations
• Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach (2017) — 653 citations
• Transparent, explainable, and accountable AI for robotics (2017) — 291 citations
• The ethics of algorithms: Mapping the debate (2016) — 2,485 citations