Nina Grgic-Hlaca

Safety, Robustness & Interpretability · 2016–2020

A researcher in the Science corpus: 7 papers; trusts 34 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Safety, Robustness & Interpretability — 63 citations into it
• Safety Research — 58 citations into it; #1,547 of 5,829 by trust here, #768 by citations
• Artificial Intelligence — 55 citations into it
• Sociology and Political Science — 50 citations into it
• AI Ethics, Fairness & Society — 39 citations into it; #466 of 494 by trust here, #214 by citations
• Economics and Econometrics — 12 citations into it; #18,541 of 23,481 by trust here, #20,008 by citations
• Statistics and Probability — 11 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Nina Grgic-Hlaca trusts overall

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

1. Muhammad Bilal Zafar
2. Alexandra Chouldechova
3. Jon M. Kleinberg
4. Isabel Valera
5. Panagiotis G. Ipeirotis
6. Moritz Hardt
7. Cynthia Dwork
8. Faisal Kamiran
9. Richard S. Zemel
10. Anthony W. Flores

Who trusts Nina Grgic-Hlaca overall

1. Dana Pessach
2. Adrian Weller
3. Luca Oneto
4. Xinru Wang
5. Nima Kordzadeh
6. Emma Pierson
7. Erez Shmueli
8. Nripsuta Ani Saxena
9. Javier Antorán
10. Yahya H. Ezzeldin

Papers from Nina Grgic-Hlaca's perspective

Ordered by how strongly the researchers Nina Grgic-Hlaca 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. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Fairness through awareness
9. Et al
10. Equality of Opportunity in Supervised Learning

Their papers

• Human Decision Making with Machine Assistance (2019) — 113 citations
• Human-Centered Approaches to Fair and Responsible AI (2020) — 58 citations
• Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning (2018) — 243 citations
• A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices (2018) — 300 citations
• Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction (2018) — 250 citations
• On Fairness, Diversity and Randomness in Algorithmic Decision Making (2017) — 44 citations
• The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making (2016) — 237 citations