{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:36:02Z","timestamp":1785404162521,"version":"3.56.0"},"reference-count":10,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Economic Affairs, Labour and Tourism Baden-W\u00fcrttemberg","award":["BW7_1030\/02"],"award-info":[{"award-number":["BW7_1030\/02"]}]},{"name":"Ministry of Economic Affairs, Labour and Tourism Baden-W\u00fcrttemberg","award":["BW7_1026\/02"],"award-info":[{"award-number":["BW7_1026\/02"]}]},{"award":["BW7_1030\/02"],"award-info":[{"award-number":["BW7_1030\/02"]}],"id":[{"id":"https:\/\/ror.org\/042ge0913","id-type":"ROR","asserted-by":"publisher"}]},{"award":["BW7_1026\/02"],"award-info":[{"award-number":["BW7_1026\/02"]}],"id":[{"id":"https:\/\/ror.org\/042ge0913","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Reutlingen University"},{"id":[{"id":"https:\/\/ror.org\/00q644y50","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Recruitment is a time-consuming process, and AI systems are increasingly being used to support the decision-making process. However, machine learning models used in such systems can inherit bias if the underlying training data reflects biased human preferences. It is essential to analyze and quantify these biases in order to develop fairer AI systems. To address this issue, we collected human judgments of colleague preference for 2200 face images. The face image set includes images of different ethnicities and genders, as well as both real and synthetically generated faces. The images were annotated by humans from diverse backgrounds in terms of age, gender, and ethnicity. Annotators were shown series of pairs of face images and asked to select which individual they would prefer as a colleague. We gathered responses from 451 annotators and aggregated the annotations to compute a preference score for each image. This dataset provides a basis for understanding human bias in colleague preference and can support the development of fair and unbiased AI models for use in recruitment settings.<\/jats:p>","DOI":"10.3390\/data11050100","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T06:50:01Z","timestamp":1777618201000},"page":"100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Preferred Colleague Dataset: A Human-Annotated Dataset of Perceived Colleague Preference"],"prefix":"10.3390","volume":"11","author":[{"given":"Deepu","family":"Krishnareddy","sequence":"first","affiliation":[{"name":"Reutlingen University, Alteburgstra\u00dfe 150, 72762 Reutlingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bakir","family":"Had\u017ei\u0107","sequence":"additional","affiliation":[{"name":"Reutlingen University, Alteburgstra\u00dfe 150, 72762 Reutlingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5524-7678","authenticated-orcid":false,"given":"Hamid","family":"Gazerpour","sequence":"additional","affiliation":[{"name":"Reutlingen University, Alteburgstra\u00dfe 150, 72762 Reutlingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Danner","sequence":"additional","affiliation":[{"name":"Autonomous Systems, Bochum University of Applied Sciences, Am Hochschulcampus 1, 44801 Bochum, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1793-4729","authenticated-orcid":false,"given":"Zhuoqi","family":"Zeng","sequence":"additional","affiliation":[{"name":"Hainan Bielefeld University of Applied Sciences, No. 1, Xizhao Road, Yangpu Economic Development Zone, Danzhou 578101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"R\u00e4tsch","sequence":"additional","affiliation":[{"name":"Reutlingen University, Alteburgstra\u00dfe 150, 72762 Reutlingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1037\/0022-3514.35.4.250","article-title":"The halo effect: Evidence for unconscious alteration of judgments","volume":"35","author":"Nisbett","year":"1977","journal-title":"J. Pers. Soc. Psychol."},{"key":"ref_2","unstructured":"Caven, V., and Nachmias, S. (2018). Cognitive Biases in Recruitment, Selection, and Promotion: The Risk of Subconscious Discrimination. Hidden Inequalities in the Workplace: A Guide to the Current Challenges, Issues and Business Solutions, Springer International Publishing."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"14169","DOI":"10.1007\/s00521-021-06535-0","article-title":"MEBeauty: A multi-ethnic facial beauty dataset in-the-wild","volume":"34","author":"Lebedeva","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liang, L., Lin, L., Jin, L., Xie, D., and Li, M. (2018, January 20\u201324). SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8546038"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xie, D., Liang, L., Jin, L., Xu, J., and Li, M. (2015). SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception. arXiv.","DOI":"10.1109\/SMC.2015.319"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O.M., and Zisserman, A. (2018). VGGFace2: A dataset for recognising faces across pose and age. arXiv.","DOI":"10.1109\/FG.2018.00020"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/TAFFC.2017.2740923","article-title":"AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild","volume":"10","author":"Mollahosseini","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Danner, M., Had\u017ei\u0107, B., Radloff, R., Su, X., Peng, L., Weber, T., and R\u00e4tsch, M. (2023). Overcome Ethnic Discrimination with Unbiased Machine Learning for Facial Data Sets. Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, SCITEPRESS\u2014Science and Technology Publications.","DOI":"10.5220\/0011624900003417"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., and Choo, J. (2018). StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. arXiv.","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Serengil, S.I., and Ozpinar, A. (2020, January 15\u201317). LightFace: A Hybrid Deep Face Recognition Framework. Proceedings of the 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Istanbul, Turkey.","DOI":"10.1109\/ASYU50717.2020.9259802"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/11\/5\/100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T07:06:54Z","timestamp":1777619214000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/11\/5\/100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,1]]},"references-count":10,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["data11050100"],"URL":"https:\/\/doi.org\/10.3390\/data11050100","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,1]]}}}