Bias in artificial intelligence algorithms and recommendations for mitigation

Abstract

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This paper, published in 1950, received 232 indexed citations. Written by Lama Nazer, Janny Xue Chen Ke, Mira Moukheiber, Ashish K. Khanna, Rachel Hicklen, Lama Moukheiber, Dana Moukheiber, Haobo Ma and Piyush Mathur covering the research area of Health Informatics, General Health Professions and Oncology. It is primarily cited by scholars working on Health Informatics (125 citations), Artificial Intelligence (60 citations) and Radiology, Nuclear Medicine and Imaging (41 citations). Published in SHILAP Revista de lepidopterología.

Countries where authors are citing Bias in artificial intelligence algorithms and recommendations for mitigation

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This map shows the geographic impact of Bias in artificial intelligence algorithms and recommendations for mitigation. 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 Bias in artificial intelligence algorithms and recommendations for mitigation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bias in artificial intelligence algorithms and recommendations for mitigation more than expected).

Fields of papers citing Bias in artificial intelligence algorithms and recommendations for mitigation

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Bias in artificial intelligence algorithms and recommendations for mitigation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Bias in artificial intelligence algorithms and recommendations for mitigation.

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.

This paper is also available at doi.org/10.1371/journal.pdig.0000278.

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