Michael Veale

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 10 papers; trusts 62 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it; #9,660 of 35,345 by trust here, #4,682 by citations
• Safety, Robustness & Interpretability — 75 citations into it; #1,778 of 2,251 by trust here, #1,512 by citations
• Sociology and Political Science — 50 citations into it; #15,177 of 22,756 by trust here, #4,924 by citations
• Safety Research — 42 citations into it; #851 of 5,829 by trust here, #294 by citations
• AI Ethics, Fairness & Society — 37 citations into it
• Information Systems — 29 citations into it
• Privacy & Security — 25 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it

Whom Michael Veale trusts overall

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

1. Faisal Kamiran
2. Ilya Mironov
3. Cynthia Dwork
4. Toon Calders
5. Grgoire Montavon
6. Bettina Berendt
7. Michael Feldman
8. João Gama
9. Andrew D. Selbst
10. Indrė Žliobaitė

Who trusts Michael Veale overall

1. Reuben Binns
2. Kenneth Holstein
3. Jennifer Wortman Vaughan
4. Dang Lien Minh
5. McKane Andrus
6. Sara Hooker
7. Nima Kordzadeh
8. Philipp Hacker
9. Samuel Budd
10. David Leslie

Papers from Michael Veale's perspective

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

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

Their papers

• Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data (2017) — 419 citations
• Blind Justice: Fairness with Encrypted Sensitive Attributes (2018) — 167 citations
• Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making (2018) — 500 citations
• Algorithms that remember: model inversion attacks and data protection law (2018) — 238 citations
• Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence (2020) — 486 citations
• Demystifying the Draft EU Artificial Intelligence Act — Analysing the good, the bad, and the unclear elements of the proposed approach (2021) — 626 citations
• Decentralized Privacy-Preserving Proximity Tracing (2020) — 366 citations
• Enslaving the Algorithm: From a “Right to an Explanation” to a “Right to Better Decisions”? (2018) — 156 citations
• 'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions (2018) — 779 citations
• Like Trainer, Like Bot? Inheritance of Bias in Algorithmic Content Moderation (2017) — 180 citations