Morgan Klaus Scheuerman

Safety, Robustness & Interpretability · 2018–2023

A researcher in the Science corpus: 9 papers; trusts 104 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Safety, Robustness & Interpretability — 77 citations into it; #1,542 of 2,251 by trust here, #915 by citations
• Computer Vision and Pattern Recognition — 72 citations into it; #9,223 of 21,575 by trust here, #5,806 by citations
• Sociology and Political Science — 64 citations into it; #17,599 of 22,756 by trust here, #6,353 by citations
• Artificial Intelligence — 54 citations into it
• Safety Research — 39 citations into it; #5,525 of 5,829 by trust here, #1,460 by citations
• AI Ethics, Fairness & Society — 38 citations into it; #361 of 494 by trust here, #214 by citations
• Evaluation & Benchmarks — 36 citations into it; #2,938 of 3,147 by trust here, #1,764 by citations
• Human-Computer Interaction — 32 citations into it; #788 of 2,253 by trust here, #195 by citations

Whom Morgan Klaus Scheuerman trusts overall

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

1. Joy Buolamwini
2. Timnit Gebru
3. Eli Blevis
4. J. Marc Overhage
5. Peter N. Belhumeur
6. Kevin A. McLemore
7. Emily M. Bender
8. Wilma A. Bainbridge
9. Geoffrey E. Hinton
10. Antonio Torralba

Who trusts Morgan Klaus Scheuerman overall

1. Emily L. Denton
2. Angelina Wang
3. Ben Hutchinson
4. Zaid Khan
5. Abeba Birhane
6. Ramya Srinivasan
7. Milagros Miceli
8. A. Hanna
9. Cynthia L. Bennett
10. Negar Rostamzadeh

Papers from Morgan Klaus Scheuerman's perspective

Ordered by how strongly the researchers Morgan Klaus Scheuerman 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development (2021) — 261 citations
• Gender Recognition or Gender Reductionism?: The Social Implications of Embedded Gender Recognition Systems (2018) — 260 citations
• How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis (2020) — 209 citations
• “It’s Complicated”: Negotiating Accessibility and (Mis)Representation in Image Descriptions of Race, Gender, and Disability (2021) — 146 citations
• Taxonomizing and Measuring Representational Harms: A Look at Image Tagging (2023) — 67 citations
• Revisiting Gendered Web Forms: An Evaluation of Gender Inputs with (Non-)Binary People (2021) — 39 citations
• HCI Guidelines for Gender Equity and Inclusivity (2020)
• Bringing the People Back In: Contesting Benchmark Machine Learning Datasets (2020) — 102 citations
• How Computers See Gender (2019) — 158 citations