David Madras

1.8k total citations
9 papers, 133 citations indexed

About

David Madras is a scholar working on Artificial Intelligence, Safety Research and Economics and Econometrics. According to data from OpenAlex, David Madras has authored 9 papers receiving a total of 133 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Safety Research and 2 papers in Economics and Econometrics. Recurrent topics in David Madras's work include Explainable Artificial Intelligence (XAI) (5 papers), Ethics and Social Impacts of AI (4 papers) and Adversarial Robustness in Machine Learning (3 papers). David Madras is often cited by papers focused on Explainable Artificial Intelligence (XAI) (5 papers), Ethics and Social Impacts of AI (4 papers) and Adversarial Robustness in Machine Learning (3 papers). David Madras collaborates with scholars based in Canada, United States and Germany. David Madras's co-authors include Richard S. Zemel, Toniann Pitassi, Elliot Creager, Kevin Swersky, Jörn-Henrik Jacobsen, Marzyeh Ghassemi, Dylan Hadfield-Menell, Gillian K. Hadfield, Aparna Balagopalan and Jacquelyn Burkell and has published in prestigious journals such as SHILAP Revista de lepidopterología, Science Advances and arXiv (Cornell University).

In The Last Decade

David Madras

7 papers receiving 128 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Madras Canada 4 88 52 21 19 11 9 133
Saffron Huang United Kingdom 2 103 1.2× 29 0.6× 15 0.7× 17 0.9× 9 0.8× 2 145
Joe Barrow United States 5 134 1.5× 17 0.3× 13 0.6× 21 1.1× 20 1.8× 9 210
Elliot Creager Canada 6 131 1.5× 40 0.8× 45 2.1× 14 0.7× 7 0.6× 11 166
Isabel O. Gallegos United States 3 79 0.9× 17 0.3× 6 0.3× 21 1.1× 17 1.5× 6 156
Seth Neel United States 7 92 1.0× 45 0.9× 8 0.4× 8 0.4× 14 1.3× 12 119
Preethi Lahoti Finland 6 68 0.8× 34 0.7× 7 0.3× 7 0.4× 13 1.2× 7 98
Pradyumna Tambwekar United States 5 152 1.7× 45 0.9× 13 0.6× 31 1.6× 9 0.8× 10 190
Arjun Roy Germany 5 104 1.2× 66 1.3× 9 0.4× 10 0.5× 16 1.5× 10 151
Kamrun Naher Keya United States 6 119 1.4× 92 1.8× 21 1.0× 16 0.8× 27 2.5× 10 200
Pablo Pedemonte United States 5 84 1.0× 22 0.4× 11 0.5× 20 1.1× 2 0.2× 7 106

Countries citing papers authored by David Madras

Since Specialization
Citations

This map shows the geographic impact of David Madras's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Madras with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Madras more than expected).

Fields of papers citing papers by David Madras

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Madras. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Madras. The network helps show where David Madras may publish in the future.

Co-authorship network of co-authors of David Madras

This figure shows the co-authorship network connecting the top 25 collaborators of David Madras. A scholar is included among the top collaborators of David Madras based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Madras. David Madras is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
2.
Balagopalan, Aparna, et al.. (2023). Judging facts, judging norms: Training machine learning models to judge humans requires a modified approach to labeling data. Science Advances. 9(19). eabq0701–eabq0701. 8 indexed citations
3.
Creager, Elliot, David Madras, Toniann Pitassi, & Richard S. Zemel. (2020). Causal Modeling for Fairness In Dynamical Systems. International Conference on Machine Learning. 1. 2185–2195. 1 indexed citations
4.
McCoy, Liam G., Jacquelyn Burkell, Dallas Card, et al.. (2019). On Meaningful Human Control in High-Stakes Machine-Human Partnerships. eScholarship (California Digital Library). 2 indexed citations
5.
Madras, David, Elliot Creager, Toniann Pitassi, & Richard S. Zemel. (2019). Fairness through Causal Awareness. 349–358. 44 indexed citations
6.
Creager, Elliot, David Madras, Jörn-Henrik Jacobsen, et al.. (2019). Flexibly Fair Representation Learning by Disentanglement. arXiv (Cornell University). 1436–1445. 46 indexed citations
7.
Chan, Timothy C. Y., David Madras, & Martin L. Puterman. (2018). Improving fairness in match play golf through enhanced handicap allocation. SHILAP Revista de lepidopterología. 4(4). 251–262. 1 indexed citations
8.
Madras, David, Toniann Pitassi, & Richard S. Zemel. (2017). Predict Responsibly: Increasing Fairness by Learning To Defer. arXiv (Cornell University). 2 indexed citations
9.
Madras, David, Toniann Pitassi, & Richard S. Zemel. (2017). Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer. arXiv (Cornell University). 31. 6147–6157. 29 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Saffron Huang Breakdown of academic impact, for papers by Joe Barrow Breakdown of academic impact, for papers by Elliot Creager Breakdown of academic impact, for papers by Isabel O. Gallegos Breakdown of academic impact, for papers by Seth Neel Breakdown of academic impact, for papers by Preethi Lahoti Breakdown of academic impact, for papers by Pradyumna Tambwekar Breakdown of academic impact, for papers by Arjun Roy Breakdown of academic impact, for papers by Kamrun Naher Keya Breakdown of academic impact, for papers by Pablo Pedemonte
Rankless by CCL
2026