Ben Green

Sociology and Political Science · with Chen · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 74 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Sociology and Political Science — 59 citations into it; #3,472 of 22,756 by trust here, #7,052 by citations
• AI Ethics, Fairness & Society — 43 citations into it; #63 of 494 by trust here, #156 by citations
• Artificial Intelligence — 42 citations into it; #9,955 of 35,345 by trust here, #16,471 by citations
• Safety, Robustness & Interpretability — 42 citations into it
• Safety Research — 38 citations into it; #221 of 5,829 by trust here, #778 by citations
• Social Sciences & Humanities — 31 citations into it; #248 of 309 by trust here, #176 by citations
• Information Systems — 17 citations into it
• Human-Computer Interaction — 16 citations into it; #422 of 2,253 by trust here, #1,275 by citations

Whom Ben Green trusts overall

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

1. Jerome H. Friedman
2. Bo Cowgill
3. Ilan Yaniv
4. M. Stevenson
5. Alexandra Chouldechova
6. Sarah Singh
7. Jon M. Kleinberg
8. Tony Doyle
9. Sonja B. Starr
10. Anna Lauren Hoffmann

Who trusts Ben Green overall

1. Vivian Lai
2. Hussein Mozannar
3. Angelina Wang
4. Han Liu
5. Zana Buçinca
6. Alexandra Olteanu
7. Ryan Shaun Joazeiro de Baker
8. Xinru Wang
9. David A. Sontag
10. Chenhao Tan

Papers from Ben Green's perspective

Ordered by how strongly the researchers Ben Green 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. ImageNet: A large-scale hierarchical image database
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Equality of Opportunity in Supervised Learning
9. Et al
10. Going deeper with convolutions

Their papers

• Algorithmic realism: expanding the boundaries of algorithmic thought (2020) — 172 citations
• Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments (2019) — 303 citations
• The false promise of risk assessments: epistemic reform and the limits of fairness (2020) — 97 citations
• The Principles and Limits of Algorithm-in-the-Loop Decision Making (2019) — 424 citations
• Data Science as Political Action: Grounding Data Science in a Politics of Justice (2018) — 132 citations
• The Myth in the Methodology: Towards a Recontextualization of Fairness in Machine Learning (2018) — 108 citations