Reuben Binns

1.3k total citations
14 papers, 210 citations indexed

About

Reuben Binns is a scholar working on Sociology and Political Science, Safety Research and Artificial Intelligence. According to data from OpenAlex, Reuben Binns has authored 14 papers receiving a total of 210 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Sociology and Political Science, 7 papers in Safety Research and 5 papers in Artificial Intelligence. Recurrent topics in Reuben Binns's work include Ethics and Social Impacts of AI (7 papers), Privacy, Security, and Data Protection (5 papers) and Digital Economy and Work Transformation (3 papers). Reuben Binns is often cited by papers focused on Ethics and Social Impacts of AI (7 papers), Privacy, Security, and Data Protection (5 papers) and Digital Economy and Work Transformation (3 papers). Reuben Binns collaborates with scholars based in United Kingdom, United States and Germany. Reuben Binns's co-authors include Michael Veale, Jeremias Adams‐Prassl, Max Van Kleek, Nigel Shadbolt, Min Kyung Lee, Michelle Mohr Carney, Kori Inkpen, Nina Grgić-Hlača, Adrian Weller and Michael Carl Tschantz and has published in prestigious journals such as IEEE Security & Privacy, Regulation & Governance and Modern Law Review.

In The Last Decade

Reuben Binns

14 papers receiving 197 citations

Peers

Reuben Binns

SR

SPS

AI

IS

CN

Elettra Bietti United States

Aleš Završník Slovenia

Paul Nemitz Belgium

Peter Cihon United Kingdom

Federico Casolari Italy

Reuben Binns United Kingdom

Jascha Bareis Germany

Daragh Murray United Kingdom

Xavier Ferrer United Kingdom

Birte Keller Germany

Elettra Bietti United States

Reuben Binns

149 ×1.4

SR

77 ×1.1

SPS

62 ×1.1

AI

48 ×1.2

IS

64 ×2.5

CN

Citations per year, relative to Reuben Binns Reuben Binns (= 1×) peers Elettra Bietti

Countries citing papers authored by Reuben Binns

Since Specialization

Citations

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

Fields of papers citing papers by Reuben Binns

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Reuben Binns. 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 Reuben Binns. The network helps show where Reuben Binns may publish in the future.

Co-authorship network of co-authors of Reuben Binns

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

All Works

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14 of 14 papers shown

1.

Binns, Reuben, et al.. (2023). Legal Taxonomies of Machine Bias: Revisiting Direct Discrimination. 1850–1858. 2 indexed citations

2.

Veale, Michael, M. Six Silberman, & Reuben Binns. (2023). Fortifying the algorithmic management provisions in the proposed Platform Work Directive. European Labour Law Journal. 14(2). 308–332. 5 indexed citations

3.

Adams‐Prassl, Jeremias, et al.. (2022). Directly Discriminatory Algorithms. Modern Law Review. 86(1). 144–175. 23 indexed citations

4.

Kleek, Max Van, et al.. (2022). Goodbye Tracking? Impact of iOS App Tracking Transparency and Privacy Labels. Research Publications (Maastricht University). 508–520. 37 indexed citations

5.

Binns, Reuben & Michael Veale. (2021). Is that your final decision? Multi-stage profiling, selective effects, and Article 22 of the GDPR. International Data Privacy Law. 11(4). 319–332. 16 indexed citations

6.

Binns, Reuben. (2020). Algorithmic Decision-making: A Guide For Lawyers. Judicial Review. 25(1). 2–7. 3 indexed citations

7.

Lee, Min Kyung, Nina Grgić-Hlača, Michael Carl Tschantz, et al.. (2020). Human-Centered Approaches to Fair and Responsible AI. 1–8. 24 indexed citations

8.

Binns, Reuben. (2020). Human Judgment in algorithmic loops: Individual justice and automated decision‐making. Regulation & Governance. 16(1). 197–211. 52 indexed citations

9.

Binns, Reuben. (2019). Human Judgement in Algorithmic Loops; Individual Justice and Automated Decision-Making. SSRN Electronic Journal. 2 indexed citations

10.

Binns, Reuben. (2018). What Can Political Philosophy Teach Us about Algorithmic Fairness?. IEEE Security & Privacy. 16(3). 73–80. 22 indexed citations

11.

Veale, Michael & Reuben Binns. (2017). Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. SocArXiv (OSF Preprints). 20 indexed citations

12.

Binns, Reuben. (2016). Self-authored interest profiles for personalised recommendations. International Journal of Internet Marketing and Advertising. 10(3). 207–207. 1 indexed citations

13.

Binns, Reuben, et al.. (2014). Data havens, or privacy sans frontières?. 273–274. 2 indexed citations

14.

Binns, Reuben, et al.. (2012). Opening up the online notice infrastructure. ePrints Soton (University of Southampton). 1 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.

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