Jamila Smith-Loud

1.4k total citations · 1 hit paper
2 papers, 436 citations indexed

About

Jamila Smith-Loud is a scholar working on Safety Research, Sociology and Political Science and Health Informatics. According to data from OpenAlex, Jamila Smith-Loud has authored 2 papers receiving a total of 436 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Safety Research, 1 paper in Sociology and Political Science and 1 paper in Health Informatics. Recurrent topics in Jamila Smith-Loud's work include Ethics and Social Impacts of AI (2 papers), Adversarial Robustness in Machine Learning (1 paper) and Artificial Intelligence in Healthcare and Education (1 paper). Jamila Smith-Loud is often cited by papers focused on Ethics and Social Impacts of AI (2 papers), Adversarial Robustness in Machine Learning (1 paper) and Artificial Intelligence in Healthcare and Education (1 paper). Jamila Smith-Loud collaborates with scholars based in United States. Jamila Smith-Loud's co-authors include Ben Hutchinson, Andrew Smart, Timnit Gebru, Parker Barnes, Margaret Mitchell, Inioluwa Deborah Raji, Lauren Wilcox, Patrick Gage Kelley, Allison Woodruff and Renee Shelby and has published in prestigious journals such as .

In The Last Decade

Jamila Smith-Loud

2 papers receiving 411 citations

Hit Papers

Closing the AI accountability gap 2020 2026 2022 2024 2020 100 200 300 400

Peers

Jamila Smith-Loud
Parker Barnes United States
Michelle Seng Ah Lee United Kingdom
Ana Marasović United States
Jess Whittlestone United Kingdom
Johann Laux United Kingdom
Reuben Binns United Kingdom
Parker Barnes United States
Jamila Smith-Loud
Citations per year, relative to Jamila Smith-Loud Jamila Smith-Loud (= 1×) peers Parker Barnes

Countries citing papers authored by Jamila Smith-Loud

Since Specialization
Citations

This map shows the geographic impact of Jamila Smith-Loud'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 Jamila Smith-Loud with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jamila Smith-Loud more than expected).

Fields of papers citing papers by Jamila Smith-Loud

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jamila Smith-Loud. 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 Jamila Smith-Loud. The network helps show where Jamila Smith-Loud may publish in the future.

Co-authorship network of co-authors of Jamila Smith-Loud

This figure shows the co-authorship network connecting the top 25 collaborators of Jamila Smith-Loud. A scholar is included among the top collaborators of Jamila Smith-Loud 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 Jamila Smith-Loud. Jamila Smith-Loud is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

2 of 2 papers shown
1.
Woodruff, Allison, et al.. (2024). How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries. 1–26. 29 indexed citations
2.
Raji, Inioluwa Deborah, Andrew Smart, Margaret Mitchell, et al.. (2020). Closing the AI accountability gap. 33–44. 407 indexed citations breakdown →

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.

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