Andrew Smart

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 10 papers; trusts 46 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 162 citations into it; #34,944 of 35,345 by trust here, #10,942 by citations
• Safety, Robustness & Interpretability — 160 citations into it; #1,367 of 2,251 by trust here, #621 by citations
• Safety Research — 130 citations into it; #2,576 of 5,829 by trust here, #367 by citations
• Sociology and Political Science — 95 citations into it
• AI Ethics, Fairness & Society — 76 citations into it
• Computer Vision and Pattern Recognition — 58 citations into it
• Natural Language Processing — 49 citations into it
• Social Sciences & Humanities — 47 citations into it

Whom Andrew Smart trusts overall

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

1. Nitin Kohli
2. Alexandra Chouldechova
3. Brent Daniel Mittelstadt
4. Solon Barocas
5. Moritz Hardt
6. Samir Passi
7. Nicholas A. Diakopoulos
8. Nancy G. Leveson
9. Geoffrey E. Hinton
10. Maranke Wieringa

Who trusts Andrew Smart overall

1. Angelina Wang
2. Daniel S. Schiff
3. Ben Hutchinson
4. Emily L. Denton
5. Zaid Khan

Papers from Andrew Smart's perspective

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

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

Their papers

• Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020) — 1,364 citations
• On the genealogy of machine learning datasets: A critical history of ImageNet (2021) — 220 citations
• The Use and Misuse of Counterfactuals in Ethical Machine Learning (2021) — 110 citations
• Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2020) — 329 citations
• Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (2022) — 390 citations
• Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction (2022) — 35 citations
• Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context (2020) — 42 citations
• Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics (2020) — 75 citations
• Bringing the People Back In: Contesting Benchmark Machine Learning Datasets (2020) — 102 citations
• Towards a critical race methodology in algorithmic fairness (2019) — 342 citations