Deborah Hellman

2008–2021

A researcher in the Science corpus: 3 papers; trusts 0 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Who trusts Deborah Hellman overall

1. Reuben Binns
2. Michael Veale
3. Berk Ustun
4. Emily M. Bender
5. Deirdre K. Mulligan
6. Orestis Papakyriakopoulos
7. Momin M. Malik
8. S. Segall
9. Elliot Creager
10. Solon Barocas

Papers from Deborah Hellman's perspective

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

1. Natural Adversarial Examples
2. Adversarial Examples Are Not Bugs, They Are Features
3. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
4. Equality of When
5. ImageNet classification with deep convolutional neural networks
6. Gradient-based learning applied to document recognition
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems (2021) — 104 citations
• Indirect Discrimination and the Duty to Avoid Compounding Injustice (2017)
• When Is Discrimination Wrong (2008) — 204 citations