Abigail Z. Jacobs
Safety, Robustness & Interpretability · 2014–2019
A researcher in the Science corpus: 2 papers; trusts 25 researchers and is trusted by 15 researchers here, as inferred from the citation graph.
Subfields
- Safety, Robustness & Interpretability — 29 citations into it; #930 of 2,251 by trust here, #1,148 by citations
- Safety Research — 20 citations into it; #1,390 of 5,829 by trust here, #1,393 by citations
- Artificial Intelligence — 17 citations into it; #10,325 of 35,345 by trust here, #11,921 by citations
- Social Sciences & Humanities — 14 citations into it
- Sociology and Political Science — 14 citations into it; #6,032 of 22,756 by trust here, #5,814 by citations
- AI Ethics, Fairness & Society — 12 citations into it
- Statistical and Nonlinear Physics — 9 citations into it; #4,785 of 10,127 by trust here, #7,747 by citations
- Evaluation & Benchmarks — 5 citations into it
Whom Abigail Z. Jacobs trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- A. Amrein-Beardsley
- Alexandra Chouldechova
- Sendhil Mullainathan
- Solon Barocas
- Jon M. Kleinberg
- Andrew D. Selbst
- Kevin M. Quinn
- Mark Newman
- Hagai Thomas Attias
- Christian P. Robert
Who trusts Abigail Z. Jacobs overall
- Su Lin Blodgett
- Muhan Zhang
- Seraphina Goldfarb-Tarrant
- Oskar van der Wal
- Angelina Wang
- Simon Zhuang
- Inioluwa Deborah Raji
- Momin M. Malik
- Jonathan Stray
- Yixin Chen
Papers from Abigail Z. Jacobs's perspective
Ordered by how strongly the researchers Abigail Z. Jacobs trusts endorse each paper. Same corpus, a different order for every researcher.
- Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
- Inherent Trade-Offs in the Fair Determination of Risk Scores
- Fairness through awareness
- Big Data's Disparate Impact
- Nber Working Paper Series Human Decisions and Machine Predictions Human Decisions and Machine Predictions
- Equality of Opportunity in Supervised Learning
- Fairness in Learning: Classic and Contextual Bandits
- Algorithmic Decision Making and the Cost of Fairness
- Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
- ImageNet classification with deep convolutional neural networks
Their papers
- Measurement and Fairness (2019) — 535 citations
- Learning Latent Block Structure in Weighted Networks (2014) — 336 citations