Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Big Data's Disparate Impact
20161.1k citationsSolon Barocas, Andrew D. SelbstSSRN Electronic Journalprofile →
This map shows the geographic impact of Solon Barocas'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 Solon Barocas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Solon Barocas more than expected).
This network shows the impact of papers produced by Solon Barocas. 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 Solon Barocas. The network helps show where Solon Barocas may publish in the future.
Co-authorship network of co-authors of Solon Barocas
This figure shows the co-authorship network connecting the top 25 collaborators of Solon Barocas.
A scholar is included among the top collaborators of Solon Barocas 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 Solon Barocas. Solon Barocas is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Barocas, Solon, et al.. (2024). On the Actionability of Outcome Prediction. Proceedings of the AAAI Conference on Artificial Intelligence. 38(20). 22240–22249.1 indexed citations
Barocas, Solon & Karen Levy. (2020). Privacy Dependencies. Washington law review. 95(2).4 indexed citations
11.
Mitchell, Shira, Eric Potash, Solon Barocas, Alexander D’Amour, & Kristian Lum. (2020). Algorithmic Fairness: Choices, Assumptions, and Definitions. Annual Review of Statistics and Its Application. 8(1). 141–163.286 indexed citations breakdown →
12.
Selbst, Andrew D. & Solon Barocas. (2018). The Intuitive Appeal of Explainable Machines. Fordham law review. 87(3). 1085.49 indexed citations
13.
Levy, Karen & Solon Barocas. (2018). Privacy at the Margins| Refractive Surveillance: Monitoring Customers to Manage Workers. International journal of communication. 12. 23.17 indexed citations
14.
Barocas, Solon, et al.. (2018). Debiasing Desire: Addressing Bias and Discrimination on Intimate Platforms. SSRN Electronic Journal.3 indexed citations
15.
Bird, Sarah, Solon Barocas, Kate Crawford, Fernando Díaz, & Hanna Wallach. (2016). Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in AI. SSRN Electronic Journal. 4.13 indexed citations
16.
Barocas, Solon & Andrew D. Selbst. (2016). Big Data's Disparate Impact. SSRN Electronic Journal.1086 indexed citations breakdown →
17.
Rosenblat, Alex, Karen Levy, Solon Barocas, & Tim Hwang. (2016). Discriminating Tastes: Customer Ratings as Vehicles for Bias. SSRN Electronic Journal.19 indexed citations
18.
Lane, Julia, Katherine J. Strandburg, Solon Barocas, et al.. (2014). Privacy, Big Data, and the Public Good. Cambridge University Press eBooks.80 indexed citations
Barocas, Solon & Helen Nissenbaum. (2009). On Notice: The Trouble with Notice and Consent. SSRN Electronic Journal.33 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.