Jess Whittlestone

AI Ethics, Fairness & Society · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 1 researcher and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• AI Ethics, Fairness & Society — 8 citations into it; #456 of 494 by trust here, #396 by citations

Whom Jess Whittlestone trusts overall

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

1. Nick Bostrom
2. Adrian Weller
3. Reuben Binns
4. Joy Buolamwini
5. Aviv Ovadya
6. Miles Brundage

Who trusts Jess Whittlestone overall

1. Irene Solaiman
2. Inioluwa Deborah Raji

Papers from Jess Whittlestone's perspective

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

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

Their papers

• Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learning (2019) — 31 citations
• Why and How Governments Should Monitor AI Development (2021) — 35 citations
• The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions (2019) — 348 citations
• The tension between openness and prudence in AI research (2019) — 10 citations