Jill A. Kuhlberg

Safety, Robustness & Interpretability · 2020

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Safety, Robustness & Interpretability — 18 citations into it
• Sociology and Political Science — 13 citations into it
• Safety Research — 12 citations into it
• Artificial Intelligence — 11 citations into it
• Management Science and Operations Research — 10 citations into it
• Causal Inference & Discovery — 6 citations into it

Whom Jill A. Kuhlberg trusts overall

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

1. Silvia Chiappa
2. John H. Holland
3. David Madras
4. Alexandra Chouldechova

Papers from Jill A. Kuhlberg's perspective

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

1. Equality of Opportunity in Supervised Learning
2. Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
3. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
4. Big Data's Disparate Impact
5. Avoiding Discrimination through Causal Reasoning
6. To predict and serve?
7. The Frontiers of Fairness in Machine Learning
8. False Positives, False Negatives, and False Analyses: A Rejoinder to "Machine Bias: There's Software Used across the Country to Predict Future Criminals. and It's Biased against Blacks"
9. Runaway Feedback Loops in Predictive Policing
10. Delayed Impact of Fair Machine Learning

Their papers

• 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