R. Stuart Geiger

Sociology and Political Science · 2014–2019

A researcher in the Science corpus: 3 papers; trusts 28 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Sociology and Political Science — 23 citations into it; #4,927 of 22,756 by trust here, #8,704 by citations
• Artificial Intelligence — 20 citations into it
• Safety Research — 20 citations into it
• Information Systems — 15 citations into it
• Communication — 12 citations into it; #658 of 3,460 by trust here, #2,267 by citations
• Computer Science Applications — 12 citations into it; #360 of 1,855 by trust here, #617 by citations
• Safety, Robustness & Interpretability — 12 citations into it
• AI Ethics, Fairness & Society — 11 citations into it

Whom R. Stuart Geiger trusts overall

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

1. Tarleton Gillespie
2. Wes McKinney
3. Donald MacKenzie
4. Christopher M. Kelty
5. Koen Frenken
6. Kevin Crowston
7. Susan Leigh Star
8. Cliff Lampe
9. J. Dijck
10. J. Marc Overhage

Who trusts R. Stuart Geiger overall

1. Rob Kitchin
2. Aaron L Halfaker
3. Jakko Kemper
4. Kate Crawford
5. Oscar Alvarado
6. Emily L. Denton
7. Robert Gorwa
8. David Leslie
9. Vili Lehdonvirta
10. Momin M. Malik

Papers from R. Stuart Geiger's perspective

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

1. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
2. The politics of ‘platforms’ (New Media & Society)
3. Algorithmic Decision Making and the Cost of Fairness
4. Algorithms in practice: Comparing web journalism and criminal justice
5. Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions that Shape Social Media
6. Sorting Things Out: Classification and Its Consequences
7. The NumPy Array: A Structure for Efficient Numerical Computation
8. Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass
9. Data Structures for Statistical Computing in Python
10. Hidden Technical Debt in Machine Learning Systems

Their papers

• Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? (2019) — 183 citations
• Bots, bespoke, code and the materiality of software platforms (2014) — 120 citations
• ORES (2019) — 20 citations