Richmond Y. Wong

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it
• Safety Research — 21 citations into it
• Sociology and Political Science — 18 citations into it
• Computer Science Applications — 7 citations into it
• Prompting & Reasoning — 7 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Richmond Y. Wong trusts overall

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

1. Tongshuang Sherry Wu
2. John J. Dudley
3. Q. Liao
4. Angèle Christin

Who trusts Richmond Y. Wong overall

1. Philippe Laban
2. Nils Knoth

Papers from Richmond Y. Wong's perspective

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

1. Language Models are Few-Shot Learners (Neural Information Processing Systems)
2. Equality of Opportunity in Supervised Learning
3. Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
4. Inherent Trade-Offs in the Fair Determination of Risk Scores
5. On the Opportunities and Risks of Foundation Models
6. Fairness through awareness
7. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
8. Algorithmic Decision Making and the Cost of Fairness
9. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
10. Zero-Shot Text-to-Image Generation

Their papers

• This Thing Called Fairness (2019) — 92 citations
• Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts (2023) — 1,195 citations