Inioluwa Deborah Raji
Artificial Intelligence · 2018–2022
A researcher in the Science corpus: 11 papers; trusts 152 researchers and is trusted by 22 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 186 citations into it; #16,674 of 35,345 by trust here, #2,443 by citations
- Safety, Robustness & Interpretability — 117 citations into it; #671 of 2,251 by trust here, #202 by citations
- Evaluation & Benchmarks — 111 citations into it
- Safety Research — 101 citations into it; #606 of 5,829 by trust here, #67 by citations
- Computer Vision and Pattern Recognition — 90 citations into it
- Natural Language Processing — 77 citations into it
- AI Ethics, Fairness & Society — 69 citations into it
- Sociology and Political Science — 46 citations into it; #6,381 of 22,756 by trust here, #6,533 by citations
Whom Inioluwa Deborah Raji trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Joy Buolamwini
- Timnit Gebru
- Emily M. Bender
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Diederik P. Kingma
- Brent Daniel Mittelstadt
- Kaiming He
- Jia Deng
Who trusts Inioluwa Deborah Raji overall
- Emily L. Denton
- Ben Hutchinson
- Irene Solaiman
- Daniel S. Schiff
- Emilio Ferrara
- Mary Anne Smart
- Jakob Mokander
- A. Hanna
- Thomas Liao
- R. Shelby
Papers from Inioluwa Deborah Raji's perspective
Ordered by how strongly the researchers Inioluwa Deborah Raji trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Model Cards for Model Reporting (2018) — 2,935 citations
- AI and the Everything in the Whole Wide World Benchmark (2021) — 502 citations
- Data and its (dis)contents: A survey of dataset development and use in machine learning research (2020) — 667 citations
- Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing (2020) — 334 citations
- Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020) — 1,364 citations
- ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles (2019) — 42 citations
- Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products (2019) — 692 citations
- Handle with Care: Lessons for Data Science from Black Female Scholars (2020) — 5 citations
- The Fallacy of AI Functionality (2022) — 292 citations
- Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance (2022) — 180 citations
- On the Legal Compatibility of Fairness Definitions (2019) — 65 citations