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.
Demystifying the Draft EU Artificial Intelligence Act — Analysing the good, the bad, and the unclear elements of the proposed approach
2021287 citationsMichael Veale, Frederik Zuiderveen Borgesiusprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Michael Veale'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 Michael Veale with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Veale more than expected).
This network shows the impact of papers produced by Michael Veale. 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 Michael Veale. The network helps show where Michael Veale may publish in the future.
Co-authorship network of co-authors of Michael Veale
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Veale.
A scholar is included among the top collaborators of Michael Veale 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 Michael Veale. Michael Veale is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Veale, Michael. (2020). Privacy is not the problem with the Apple-Google contact-tracing toolkit. UCL Discovery (University College London).8 indexed citations
12.
Nouwens, Midas, Ilaria Liccardi, Michael Veale, David R. Karger, & Lalana Kagal. (2020). Dark Patterns Post-GDPR: Scraping Consent Interface Designs and Demonstrating their Influence. Human Factors in Computing Systems.1 indexed citations
Edwards, Lilian, et al.. (2019). Data subjects as data controllers: a Fashion(able) concept?. Internet Policy Review.8 indexed citations
15.
Kleek, Max Van, William Seymour, Michael Veale, Reuben Binns, & Nigel Shadbolt. (2018). The Need for Sensemaking in Networked Privacy and Algorithmic Responsibility. UCL Discovery (University College London).2 indexed citations
16.
Veale, Michael, et al.. (2018). Automating Data Rights. UCL Discovery (University College London).2 indexed citations
17.
Binns, Reuben, Max Van Kleek, Michael Veale, et al.. (2018). 'It's Reducing a Human Being to a Percentage'; Perceptions of Procedural Justice in Algorithmic Decisions. UCL Discovery (University College London).2 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.