{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:29:24Z","timestamp":1781108964724,"version":"3.54.1"},"reference-count":107,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["MSCA-ITN-2019-860813"],"award-info":[{"award-number":["MSCA-ITN-2019-860813"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["MSCA-ITN-2019-86031"],"award-info":[{"award-number":["MSCA-ITN-2019-86031"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MICINN\/Feder","award":["PID2021-127641OB-I00"],"award-info":[{"award-number":["PID2021-127641OB-I00"]}]},{"DOI":"10.13039\/501100004837","name":"MICINN","doi-asserted-by":"crossref","award":["TED2021-131787B-I00"],"award-info":[{"award-number":["TED2021-131787B-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Madrid Government","award":["PRICIT(2020\/00334\/001)"],"award-info":[{"award-number":["PRICIT(2020\/00334\/001)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new sources of discrimination are arising. There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. All these four Human-Centric requirements are closely related to each other. With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious case study focused on automated recruitment: FairCVtest. We train automatic recruitment algorithms using a set of multimodal synthetic profiles including image, text, and structured data, which are consciously scored with gender and racial biases. FairCVtest shows the capacity of the Artificial Intelligence (AI) behind automatic recruitment tools built this way (a common practice in many other application scenarios beyond recruitment) to extract sensitive information from unstructured data and exploit it in combination to data biases in undesirable (unfair) ways. We present an overview of recent works developing techniques capable of removing sensitive information and biases from the decision-making process of deep learning architectures, as well as commonly used databases for fairness research in AI. We demonstrate how learning approaches developed to guarantee privacy in latent spaces can lead to unbiased and fair automatic decision-making process. Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.<\/jats:p>","DOI":"10.1007\/s42979-023-01733-0","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T16:01:50Z","timestamp":1686153710000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-Based Recruitment"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6907-5826","authenticated-orcid":false,"given":"Alejandro","family":"Pe\u00f1a","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ignacio","family":"Serna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aythami","family":"Morales","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian","family":"Fierrez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfonso","family":"Ortega","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ainhoa","family":"Herrarte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel","family":"Alcantara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Ortega-Garcia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"1733_CR1","first-page":"671","volume":"104","author":"S Barocas","year":"2016","unstructured":"Barocas S, Selbst AD. Big data\u2019s disparate impact. Calif Law Rev. 2016;104:671\u2013732.","journal-title":"Calif Law Rev."},{"key":"1733_CR2","doi-asserted-by":"crossref","unstructured":"Acien A, Morales A, Vera-Rodriguez R, Bartolome I, Fierrez J. Measuring the gender and ethnicity bias in deep models for face recognition. In: Proceedings of Iberoamerican Congress on pattern recognition (IbPRIA), Madrid, Spain; 2018.","DOI":"10.1007\/978-3-030-13469-3_68"},{"key":"1733_CR3","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1109\/TTS.2020.2992344","volume":"1","author":"P Drozdowski","year":"2020","unstructured":"Drozdowski P, Rathgeb C, Dantcheva A, Damer N, Busch C. Demographic bias in biometrics: a survey on an emerging challenge. IEEE Trans Technol Soc. 2020;1:89\u2013103.","journal-title":"IEEE Trans Technol Soc"},{"key":"1733_CR4","unstructured":"Nagpal S, Singh M, Singh R, Vatsa M, Ratha NK. Deep learning for face recognition: pride or prejudiced? 2019. arXiv:1904.01219."},{"key":"1733_CR5","doi-asserted-by":"crossref","unstructured":"Zhao J, Wang T, Yatskar M, Ordonez V, Chang K. Men also like shopping: reducing gender bias amplification using corpus-level constraints. In: Proceedings of conference on empirical methods in natural language processing; Copenhagen, Denmark: Association for Computational Linguistics; 2017. p. 2979\u201389.","DOI":"10.18653\/v1\/D17-1323"},{"key":"1733_CR6","doi-asserted-by":"publisher","DOI":"10.18574\/nyu\/9781479833641.001.0001","volume-title":"Algorithms of oppression: how search engines reinforce racism","author":"SU Noble","year":"2018","unstructured":"Noble SU. Algorithms of oppression: how search engines reinforce racism. New York: NYU Press; 2018."},{"key":"1733_CR7","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/2460276.2460278","volume":"11","author":"L Sweeney","year":"2013","unstructured":"Sweeney L. Discrimination in online ad delivery. Queue. 2013;11:10\u201329.","journal-title":"Queue"},{"key":"1733_CR8","doi-asserted-by":"crossref","unstructured":"Ali M, Sapiezynski P, Bogen M, Korolova A, Mislove A, Rieke A. Discrimination through optimization: how Facebook\u2019s ad delivery can lead to skewed outcomes. In: Proceedings of the ACM conference on human\u2013computer interaction; NY, USA: Association for Computing Machinery; 2019.","DOI":"10.1145\/3359301"},{"key":"1733_CR9","volume-title":"Machine bias","author":"J Angwin","year":"2016","unstructured":"Angwin J, Larson J, Mattu S, Kirchner L. Machine bias. New York: ProPublica; 2016."},{"key":"1733_CR10","unstructured":"Evans M, Mathews AW. New York regulator probes United Health algorithm for racial bias. Wall Street J. 2019."},{"key":"1733_CR11","unstructured":"Knight W. The Apple Card didn\u2019t \u2019see\u2019 gender\u2014and that\u2019s the problem. Wired; 2019."},{"key":"1733_CR12","unstructured":"Buolamwini J, Gebru T. Gender shades: intersectional accuracy disparities in commercial gender classification. In: Proceedings of the ACM conference on fairness, accountability, and transparency; NY, USA: PMLR; 2018."},{"key":"1733_CR13","doi-asserted-by":"crossref","unstructured":"Wang M, Deng W. Mitigating bias in face recognition using skewness-aware reinforcement learning. In: IEEE conference on computer vision and pattern recognition (CVPR); Seattle, USA: IEEE; 2020. p. 9322\u201331.","DOI":"10.1109\/CVPR42600.2020.00934"},{"key":"1733_CR14","unstructured":"Serna I, Morales A, Fierrez J, Cebrian M, Obradovich N, Rahwan I. Algorithmic discrimination: formulation and exploration in deep learning-based face biometrics. In: Proceedings of the AAAI workshop on SafeAI; NY,USA: CEUR Workshop Proceedings; 2020."},{"key":"1733_CR15","doi-asserted-by":"crossref","unstructured":"Balakrishnan G, Xiong Y, Xia W, Perona P. Towards causal benchmarking of bias in face analysis algorithms. In: European conference on computer vision (ECCV); Glasgow, UK: Springer-Verlag; 2020. p. 547\u201363.","DOI":"10.1007\/978-3-030-58523-5_32"},{"key":"1733_CR16","unstructured":"Bogen M, Rieke A. Help wanted: examination of hiring algorithms, equity, and bias. Technical report; 2018. https:\/\/www.upturn.org"},{"key":"1733_CR17","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.bushor.2019.12.001","volume":"63","author":"JS Black","year":"2020","unstructured":"Black JS, van Esch P. AI-enabled recruiting: what is it and how should a manager use it? Bus Horiz. 2020;63:215\u201326.","journal-title":"Bus Horiz"},{"key":"1733_CR18","volume-title":"Amazon scraps secret AI recruiting tool that showed bias against women","author":"J Dastin","year":"2018","unstructured":"Dastin J. Amazon scraps secret AI recruiting tool that showed bias against women. London: Reuters; 2018."},{"key":"1733_CR19","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1257\/0002828042002561","volume":"94","author":"M Bertrand","year":"2004","unstructured":"Bertrand M, Mullainathan S. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. Am Econ Rev. 2004;94:991\u20131013.","journal-title":"Am Econ Rev"},{"key":"1733_CR20","doi-asserted-by":"crossref","unstructured":"Raghavan M, Barocas S, Kleinberg J, Levy K. Mitigating bias in algorithmic hiring: evaluating claims and practices. In: Conference on fairness, accountability, and transparency; NY, USA: Association for Computing Machinery; 2020. p. 469\u201381.","DOI":"10.1145\/3351095.3372828"},{"key":"1733_CR21","unstructured":"Schumann C, Foster JS, Mattei N, Dickerson JP. We need fairness and explainability in algorithmic hiring. In: Proceedings of the 19th international conference on autonomous agents and multiagent systems; Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems; 2020. p. 1716\u201320."},{"key":"1733_CR22","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Monedero J, Dencik L, Edwards L. What does it mean to \u2018solve\u2019 the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems. In: Conference on fairness, accountability, and transparency; NY, USA: Association for Computing Machinery 2020. p. 458\u201368.","DOI":"10.1145\/3351095.3372849"},{"key":"1733_CR23","first-page":"50","volume":"38","author":"B Goodman","year":"2016","unstructured":"Goodman B, Flaxman S. EU regulations on algorithmic decision-making and a \u201cRight to explanation.\u201d AI Mag. 2016;38:50\u20137.","journal-title":"AI Mag."},{"key":"1733_CR24","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1613\/jair.1.12814","volume":"71","author":"L Cheng","year":"2021","unstructured":"Cheng L, Varshney KR, Liu H. Socially responsible ai algorithms: issues, purposes, and challenges. J Artif Intell Res. 2021;71:1137\u201381.","journal-title":"J Artif Intell Res"},{"key":"1733_CR25","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltrus\u0306aitis","year":"2019","unstructured":"Baltrus\u0306aitis T, Ahuja C, Morency L. Multimodal machine learning: a survey and taxonomy. IEEE Trans Pattern Anal Mach Intell. 2019;41:423\u201343.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1733_CR26","doi-asserted-by":"crossref","unstructured":"Pe\u00f1a A, Serna I, Morales A, Fierrez J. Bias in multimodal AI: testbed for fair automatic recruitment. In: IEEE CVPR workshop on fair, data efficient and trusted computer vision; 2020.","DOI":"10.1109\/CVPRW50498.2020.00022"},{"issue":"1","key":"1733_CR27","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3109\/09540261.2015.1106446","volume":"28","author":"C Richards","year":"2016","unstructured":"Richards C, Bouman WP, Seal L, Barker MJ, Nieder TO, T\u2019Sjoen G. Non-binary or genderqueer genders. Int Rev Psychiatry. 2016;28(1):95\u2013102.","journal-title":"Int Rev Psychiatry"},{"key":"1733_CR28","doi-asserted-by":"crossref","unstructured":"Keyes O. The misgendering machines: trans\/hci implications of automatic gender recognition. In: Proceedings of the ACM on human\u2013computer interaction 2(CSCW); 2018. p. 1\u201322.","DOI":"10.1145\/3274357"},{"key":"1733_CR29","doi-asserted-by":"publisher","first-page":"12592","DOI":"10.1073\/pnas.1919012117","volume":"117","author":"AJ Larrazabal","year":"2020","unstructured":"Larrazabal AJ, Nieto N, Peterson V, Milone DH, Ferrante E. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci. 2020;117:12592\u20134.","journal-title":"Proc Natl Acad Sci"},{"key":"1733_CR30","unstructured":"Speicher T, Ali M, Venkatadri G, Ribeiro F.N, Arvanitakis G, Benevenuto F, Gummadi K.P, Loiseau P, Mislove A. Potential for discrimination in online targeted advertising. In: Conference on fairness, accountability and transparency; 2018. p. 5\u201319."},{"key":"1733_CR31","doi-asserted-by":"crossref","unstructured":"De-Arteaga M, Romanov R, Wallach H, Chayes J, Borgs C, et\u00a0al. Bias in bios: a case study of semantic representation bias in a high-stakes setting. In: Conference on fairness, accountability, and transparency; 2019. p. 120\u20138.","DOI":"10.1145\/3287560.3287572"},{"key":"1733_CR32","first-page":"4356","volume":"29","author":"T Bolukbasi","year":"2016","unstructured":"Bolukbasi T, Chang K, Zou JY, Saligrama V, Kalai AT. Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. Adv Neural Inf Process Syst. 2016;29:4356\u201364.","journal-title":"Adv Neural Inf Process Syst."},{"issue":"8","key":"1733_CR33","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35(8):1798\u2013828.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"48","key":"1733_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1073\/pnas.1907375117","volume":"117","author":"D Bau","year":"2020","unstructured":"Bau D, Zhu J, Strobelt H, Lapedriza A, Zhou B, Torralba A. Understanding the role of individual units in a deep neural network. Proc Natl Acad Sci. 2020;117(48):1\u20138.","journal-title":"Proc Natl Acad Sci"},{"key":"1733_CR35","unstructured":"Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H. Understanding neural networks through deep visualization. In: International conference on machine learning (ICML) deep learning workshop, Lille, France; 2015."},{"key":"1733_CR36","unstructured":"Geirhos R, Rubisch P, Michaelis C, Bethge M, Wichmann FA, Brendel W. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: International conference on learning representations (ICLR), New Orleans, Louisiana, USA; 2019."},{"issue":"7","key":"1733_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach S, Binder A, Montavon G, Klauschen F, M\u00fcller K, Samek W. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One. 2015;10(7):1\u201346.","journal-title":"PloS One"},{"key":"1733_CR38","doi-asserted-by":"crossref","unstructured":"Selvaraju R, Cogswell M, et\u00a0al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: IEEE international conference on computer vision (CVPR), Honolulu, Hawaii, USA. IEEE; 2017. p. 618\u201326.","DOI":"10.1109\/ICCV.2017.74"},{"issue":"11","key":"1733_CR39","doi-asserted-by":"publisher","first-page":"154","DOI":"10.3390\/computers10110154","volume":"10","author":"A Ortega","year":"2021","unstructured":"Ortega A, Fierrez J, Morales A, Wang Z, de la Cruz M, Alonso CL, Ribeiro T. Symbolic AI for XAI: evaluating LFIT inductive programming for explaining biases in machine learning. Computers. 2021;10(11):154.","journal-title":"Computers"},{"key":"1733_CR40","doi-asserted-by":"crossref","unstructured":"Hendricks LA, Akata Z, Rohrbach M, Donahue J, Schiele B, Darrell T. Generating visual explanations. In: European conference on computer vision (ECCV), Amsterdam, The Netherlands. Berlin: Springer; 2016. p. 3\u201319.","DOI":"10.1007\/978-3-319-46493-0_1"},{"key":"1733_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","volume":"73","author":"G Montavon","year":"2018","unstructured":"Montavon G, Samek W, M\u00fcller K. Methods for interpreting and understanding deep neural networks. Digit Signal Process. 2018;73:1\u201315.","journal-title":"Digit Signal Process."},{"key":"1733_CR42","unstructured":"Erhan D, Bengio Y, Courville A, Vincent P. Visualizing higher-layer features of a deep network, vol. 1341(3). Montreal: University of Montreal; 2009."},{"key":"1733_CR43","unstructured":"Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks: visualising image classification models and saliency maps. In: International conference on learning representations (ICLR) workshop, Banff, Canada; 2014."},{"key":"1733_CR44","doi-asserted-by":"crossref","unstructured":"Mahendran A, Vedaldi A. Understanding deep image representations by inverting them. In: IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, USA. IEEE; 2015. p. 5188\u201396.","DOI":"10.1109\/CVPR.2015.7299155"},{"key":"1733_CR45","unstructured":"Nguyen A, Yosinski J, Clune J. Multifaceted feature visualization: uncovering the different types of features learned by each neuron in deep neural networks. In: International conference on machine learning (ICML) deep learning workshop, New York, NY, USA; 2016."},{"key":"1733_CR46","unstructured":"Nguyen A, Dosovitskiy A, Yosinski J, Brox T, Clune J. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: Conference on neural information processing systems (NIPS), Barcelona, Spain; 2016. p. 3395\u2013403."},{"key":"1733_CR47","doi-asserted-by":"crossref","unstructured":"Nguyen A, Clune J, Bengio Y, Dosovitskiy A, Yosinski J. Plug & Play generative networks: conditional iterative generation of images in latent space. In: IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, Hawaii, USA. IEEE; 2017.","DOI":"10.1109\/CVPR.2017.374"},{"issue":"2","key":"1733_CR48","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/72.80236","volume":"1","author":"ED Karnin","year":"1990","unstructured":"Karnin ED. A simple procedure for pruning back-propagation trained neural networks. Trans Neural Netw. 1990;1(2):239\u201342.","journal-title":"Trans Neural Netw"},{"key":"1733_CR49","doi-asserted-by":"crossref","unstructured":"Zurada JM, Malinowski A, Cloete I. Sensitivity analysis for minimization of input data dimension for feedforward neural network. In: International symposium on circuits and systems (ISCAS), vol. 6; 1994. p. 447\u201350.","DOI":"10.1109\/ISCAS.1994.409622"},{"key":"1733_CR50","doi-asserted-by":"crossref","unstructured":"Zeiler D, Fergus R. Visualizing and understanding convolutional networks. In: European conference on computer vision (ECCV), Zurich, Switzerland. Berlin: Springer; 2014. p. 818\u201333.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"1733_CR51","unstructured":"Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M. Striving for simplicity: the all convolutional net. In: International conference on learning representations (ICLR), San Diego, CA, USA; 2015."},{"key":"1733_CR52","doi-asserted-by":"crossref","unstructured":"Zhang Q, Cao R, Shi F, Wu YN, Zhu S. Interpreting CNN knowledge via an explanatory graph. In: AAAI conference on artificial intelligence, vol. 32. New Orleans: AAAI Press; 2018.","DOI":"10.1609\/aaai.v32i1.11819"},{"key":"1733_CR53","unstructured":"Adebayo J, Gilmer J, Muelly M, Goodfellow I, Hardt M, Kim B. Sanity checks for saliency maps. In: Advances in neural information processing systems (NIPS), vol. 31. Montr\u00e9al: Curran Associates Inc.; 2018. p. 9525\u201336."},{"key":"1733_CR54","unstructured":"Szegedy C, Zaremba W, Sutskever I, Estrach JB, Erhan D, Goodfellow I, Fergus R. Intriguing properties of neural networks. In: International conference on learning representations (ICLR), Banff, Canada; 2014."},{"key":"1733_CR55","unstructured":"Pang WK, Percy L. Understanding black-box predictions via influence functions. In: International conference on machine learning (ICML), vol. 70. Sydney: PMLR; 2017. p. 1885\u201394."},{"key":"1733_CR56","doi-asserted-by":"crossref","unstructured":"Nguyen A, Yosinski J, Clune J. Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE; 2015. p. 427\u201336.","DOI":"10.1109\/CVPR.2015.7298640"},{"issue":"5","key":"1733_CR57","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","volume":"23","author":"J Su","year":"2019","unstructured":"Su J, Vargas DV, Sakurai K. One pixel attack for fooling deep neural networks. Trans Evol Comput. 2019;23(5):828\u201341.","journal-title":"Trans Evol Comput"},{"key":"1733_CR58","doi-asserted-by":"crossref","unstructured":"Quadrianto N, Sharmanska V, Thomas O. Discovering fair representations in the data domain. In: IEEE conference on computer vision and pattern recognition (CVPR) (2019). p. 8227\u201336.","DOI":"10.1109\/CVPR.2019.00842"},{"key":"1733_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1147\/JRD.2019.2945519","volume":"63","author":"P Sattigeri","year":"2019","unstructured":"Sattigeri P, Hoffman SC, Chenthamarakshan V, Varshney KR. Fairness GAN: generating datasets with fairness properties using a generative adversarial network. IBM J Res Dev. 2019;63:1\u20139.","journal-title":"IBM J Res Dev"},{"key":"1733_CR60","unstructured":"Odena A, Olah C, Shlens J. Conditional image synthesis with auxiliary classifier GANs. In: International conference on machine learning (ICML), Sydney, Australia; 2017. p. 2642\u201351."},{"key":"1733_CR61","unstructured":"Calmon FP, Wei D, Vinzamuri B, Ramamurthy KN, Varshney KR. Optimized pre-processing for discrimination prevention. In: Proceedings of the 31st international conference on neural information processing systems; 2017. p. 3995\u20134004."},{"key":"1733_CR62","doi-asserted-by":"crossref","unstructured":"Ramaswamy VV, Kim SS, Russakovsky O. Fair attribute classification through latent space de-biasing. In: IEEE conference on computer vision and pattern recognition; 2021. p. 9301\u201310.","DOI":"10.1109\/CVPR46437.2021.00918"},{"key":"1733_CR63","doi-asserted-by":"crossref","unstructured":"Jia S, Lansdall-Welfare T, Cristianini N. Right for the right reason: training agnostic networks. In: Advances in intelligent data analysis XVII; 2018. p. 164\u201374.","DOI":"10.1007\/978-3-030-01768-2_14"},{"key":"1733_CR64","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V. Domain-adversarial training of neural networks. J Mach Learn Res. 2016;17:1\u201335.","journal-title":"J Mach Learn Res."},{"key":"1733_CR65","doi-asserted-by":"crossref","unstructured":"Wang M, Deng W, Hu J, Tao X, Huang Y. Racial faces in the wild: reducing racial bias by information maximization adaptation network. In: IEEE international conference on computer vision (ICCV); 2019. p. 692\u2013702.","DOI":"10.1109\/ICCV.2019.00078"},{"key":"1733_CR66","doi-asserted-by":"crossref","unstructured":"Romanov A, De-Arteaga M, Wallach H, Chayes J, Borgs C, et\u00a0al. What\u2019s in a name? Reducing bias in bios without access to protected attributes. In: Proceedings of the 2019 conference of the North American chapter of the Association for Computational Linguistics: human language technologies; 2019. p. 4187\u201395.","DOI":"10.18653\/v1\/N19-1424"},{"key":"1733_CR67","doi-asserted-by":"crossref","unstructured":"Deng J, Guo J, Xue N, Zafeiriou S. ArcFace: additive angular margin loss for deep face recognition. In: IEEE conference on computer vision and pattern recognition (CVPR); 2019. p. 4690\u201399.","DOI":"10.1109\/CVPR.2019.00482"},{"key":"1733_CR68","first-page":"2672","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, et al. Generative adversarial nets. Adv Neural Inf Process Syst. 2014;27:2672\u201380.","journal-title":"Adv Neural Inf Process Syst"},{"key":"1733_CR69","doi-asserted-by":"crossref","unstructured":"Alvi M, Zisserman A, Nellaker C. Turning a blind eye: explicit removal of biases and variation from deep neural network embeddings. In: European conference on computer vision (ECCV); 2018.","DOI":"10.1007\/978-3-030-11009-3_34"},{"key":"1733_CR70","doi-asserted-by":"crossref","unstructured":"Kim B, Kim H, Kim K, Kim S, Kim J. Learning not to learn: training deep neural networks with biased data. In: IEEE conference on computer vision and pattern recognition (CVPR); 2019. p. 9012\u201320.","DOI":"10.1109\/CVPR.2019.00922"},{"issue":"6","key":"1733_CR71","doi-asserted-by":"publisher","first-page":"2158","DOI":"10.1109\/TPAMI.2020.3015420","volume":"43","author":"A Morales","year":"2021","unstructured":"Morales A, Fierrez J, Vera-Rodriguez R, Tolosana R. SensitiveNets: learning agnostic representations with application to face recognition. IEEE Trans Pattern Anal Mach Intell. 2021;43(6):2158\u201364.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1733_CR72","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J. FaceNet: a unified embedding for face recognition and clustering. In: IEEE conference on computer vision and pattern recognition (CVPR); 2015. p. 815\u201323.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"1733_CR73","doi-asserted-by":"crossref","unstructured":"Berendt B, Preibusch S. Exploring discrimination: a user-centric evaluation of discrimination-aware data mining. In: IEEE international conference on data mining workshops; 2012. p. 344\u201351.","DOI":"10.1109\/ICDMW.2012.109"},{"key":"1733_CR74","doi-asserted-by":"crossref","unstructured":"Pedreshi D, Ruggieri S, Turini F. Discrimination-aware data mining. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining; 2008. p. 560\u20138.","DOI":"10.1145\/1401890.1401959"},{"key":"1733_CR75","doi-asserted-by":"crossref","unstructured":"Zhang Y, Bellamy R, Varshney KR. Joint optimization of AI fairness and utility: a human-centered approach. In: Proceedings of the AAAI\/ACM conference on AI, ethics, and society; 2020. p. 400\u20136.","DOI":"10.1145\/3375627.3375862"},{"key":"1733_CR76","doi-asserted-by":"crossref","unstructured":"Yang K, Stoyanovich J. Measuring fairness in ranked outputs. In: Proceedings of the 29th international conference on scientific and statistical database management; 2017. p. 1\u20136.","DOI":"10.1145\/3085504.3085526"},{"key":"1733_CR77","doi-asserted-by":"crossref","unstructured":"Celis LE, Straszak D, Vishnoi NK. Ranking with fairness constraints. In: Proceeding of the international colloquium on automata, languages, and programming; 2018. p. 1\u201315.","DOI":"10.24963\/ijcai.2018\/20"},{"key":"1733_CR78","doi-asserted-by":"crossref","unstructured":"Zehlike M, Bonchi F, Castillo C, Hajian S, Megahed M, Baeza-Yates R. FA*IR: a fair top-k ranking algorithm. In: Proceedings of the 2017 ACM on conference on information and knowledge management; 2017. p. 1569\u201378.","DOI":"10.1145\/3132847.3132938"},{"key":"1733_CR79","unstructured":"Dua D, Graff C. UCI machine learning repository; 2017. http:\/\/archive.ics.uci.edu\/ml."},{"key":"1733_CR80","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.dss.2014.03.001","volume":"62","author":"S Moro","year":"2014","unstructured":"Moro S, Cortez P, Rita P. A data-driven approach to predict the success of bank telemarketing. Decis Support Syst. 2014;62:22\u201331.","journal-title":"Decis Support Syst"},{"key":"1733_CR81","doi-asserted-by":"crossref","unstructured":"Zhao J, Wang T, Yatskar M, Ordonez V, Chang K. Gender bias in coreference resolution: evaluation and debiasing methods. In: Conference of the North American chapter of the association for computational linguistics: human language technologies, vol. 2; 2018.","DOI":"10.18653\/v1\/N18-2003"},{"key":"1733_CR82","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X, Tang X. Deep learning face attributes in the wild. In: International conference on computer vision (ICCV); 2015.","DOI":"10.1109\/ICCV.2015.425"},{"key":"1733_CR83","doi-asserted-by":"crossref","unstructured":"Rothe R, Timofte R, Van\u00a0Gool L. Dex: deep expectation of apparent age from a single image. In: IEEE international conference on computer vision workshops (CVPRW); 2015. p. 10\u20135.","DOI":"10.1109\/ICCVW.2015.41"},{"key":"1733_CR84","unstructured":"Ricanek K Jr, Tesafaye T. Morph: a longitudinal image database of normal adult age-progression. In: International conference on automatic face and gesture recognition; 2006. p. 341\u20135."},{"key":"1733_CR85","doi-asserted-by":"crossref","unstructured":"Karkkainen K, Joo J. FairFace: face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In: IEEE winter conference on applications of computer vision; 2021. p. 1548\u201358.","DOI":"10.1109\/WACV48630.2021.00159"},{"key":"1733_CR86","unstructured":"Merler M, Ratha N, Feris SR, Smith JR. Diversity in faces. 2019. arXiv:1901.10436."},{"key":"1733_CR87","doi-asserted-by":"crossref","unstructured":"Robinson J.P, Livitz G, Henon Y, Qin C, Fu Y, Timoner S. Face recognition: too bias, or not too bias? In: IEEE conference on computer vision and pattern recognition workshops (CVPRW); 2020.","DOI":"10.1109\/CVPRW50498.2020.00008"},{"key":"1733_CR88","doi-asserted-by":"crossref","unstructured":"Hupont I, Fern\u00e1ndez C. DemogPairs: quantifying the impact of demographic imbalance in deep face recognition. In: IEEE international conference on automatic face and gesture recognition; 2019.","DOI":"10.1109\/FG.2019.8756625"},{"key":"1733_CR89","doi-asserted-by":"crossref","unstructured":"Torralba A, Efros AA. Unbiased look at dataset bias. In: IEEE conference on computer vision and pattern recognition (CVPR); 2011.","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"1733_CR90","doi-asserted-by":"publisher","first-page":"103682","DOI":"10.1016\/j.artint.2022.103682","volume":"305","author":"I Serna","year":"2022","unstructured":"Serna I, Morales A, Fierrez J, Cebrian M, Obradovich N, Rahwan I. SensitiveLoss: improving accuracy and fairness of face representations with discrimination-aware deep learning. Artif Intell. 2022;305:103682","journal-title":"Artif Intell."},{"key":"1733_CR91","doi-asserted-by":"publisher","first-page":"3635","DOI":"10.1073\/pnas.1720347115","volume":"115","author":"N Garg","year":"2018","unstructured":"Garg N, Schiebinger L, Jurafsky D, Zou J. Word embeddings quantify 100\u00a0years of gender and ethnic stereotypes. Proc Natl Acad Sci. 2018;115:3635\u201344.","journal-title":"Proc Natl Acad Sci"},{"key":"1733_CR92","doi-asserted-by":"crossref","unstructured":"Guo Y, Zhang L, Hu Y, He X, Gao J. MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: European conference on computer vision (ECCV); 2016.","DOI":"10.1007\/978-3-319-46487-9_6"},{"key":"1733_CR93","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1300\/J031v08n04_03","volume":"8","author":"M Bendick Jr","year":"1997","unstructured":"Bendick M Jr, Jackson CW, Romero JH. Employment discrimination against older workers: an experimental study of hiring practices. J Aging Soc Policy. 1997;8:25\u201346.","journal-title":"J Aging Soc Policy"},{"key":"1733_CR94","unstructured":"Cowgill B. Bias and productivity in humans and algorithms: theory and evidence from resume screening. Columbia Business School, Columbia University. 2018;29."},{"key":"1733_CR95","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.inffus.2017.12.003","volume":"44","author":"J Fierrez","year":"2018","unstructured":"Fierrez J, Morales A, Vera-Rodriguez R, Camacho D. Multiple classifiers in biometrics. Part 1: fundamentals and review. Inf Fusion. 2018;44:57\u201364.","journal-title":"Inf Fusion"},{"key":"1733_CR96","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.1109\/TIFS.2018.2807791","volume":"13","author":"E Gonzalez-Sosa","year":"2018","unstructured":"Gonzalez-Sosa E, Fierrez J, Vera-Rodriguez R, Alonso-Fernandez F. Facial soft biometrics for recognition in the wild: recent works, annotation and COTS evaluation. IEEE Trans Inf Forensics Secur. 2018;13:2001\u201314.","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"1733_CR97","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/MSP.2017.2764116","volume":"35","author":"R Ranjan","year":"2018","unstructured":"Ranjan R, Sankaranarayanan S, Bansal A, Bodla N, Chen J, Patel VM, Castillo CD, Chellappa R. Deep learning for understanding faces: machines may be just as good, or better, than humans. IEEE Signal Process Mag. 2018;35:66\u201383.","journal-title":"IEEE Signal Process Mag"},{"key":"1733_CR98","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR); 2016. p. 770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1733_CR99","unstructured":"Mikolov T, Grave E, Bojanowski P, Puhrsch C, Joulin A. Advances in pre-training distributed word representations. In: Proceedings of the international conference on language resources and evaluation (LREC 2018); 2018."},{"key":"1733_CR100","doi-asserted-by":"publisher","DOI":"10.4324\/9781315263298","volume-title":"Adverse impact and test validation: a practitioner\u2019s guide to valid and defensible employment testing","author":"D Biddle","year":"2017","unstructured":"Biddle D. Adverse impact and test validation: a practitioner\u2019s guide to valid and defensible employment testing. London: Routledge; 2017."},{"key":"1733_CR101","unstructured":"Bakker M, Valdes HR, Tu DP, Gummadi KP, Varshney KR, et\u00a0al. Fair enough: improving fairness in budget-constrained decision making using confidence thresholds. In: AAAI workshop on artificial intelligence safety, New York, NY, USA; 2020. p. 41\u201353."},{"key":"1733_CR102","doi-asserted-by":"crossref","unstructured":"Acien A, Morales A, Vera-Rodriguez R, Fierrez J, Delgado O. Smartphone sensors for modeling human\u2013computer interaction: general outlook and research datasets for user authentication. In: IEEE conference on computers, software, and applications (COMPSAC); 2020.","DOI":"10.1109\/COMPSAC48688.2020.00-81"},{"key":"1733_CR103","unstructured":"Acien A, Morales A, Fierrez J, Vera-Rodriguez R, Bartolome I. BeCAPTCHA: detecting human behavior in smartphone interaction using multiple inbuilt sensors. In: AAAI workshop on artificial intelligence for cyber security (AICS); 2020."},{"key":"1733_CR104","unstructured":"Hernandez-Ortega J, Daza R, Morales A, Fierrez J, Ortega-Garcia J. edBB: biometrics and behavior for assessing remote education. In: AAAI workshop on artificial intelligence for education (AI4EDU); 2020."},{"key":"1733_CR105","unstructured":"Serna I, DeAlcala D, Morales A, Fierrez J, Ortega-Garcia J. IFBiD: inference-free bias detection. In: AAAI workshop on artificial intelligence safety (SafeAI). CEUR, vol. 3087; 2022."},{"key":"1733_CR106","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.patcog.2017.01.024","volume":"67","author":"M Gomez-Barrero","year":"2017","unstructured":"Gomez-Barrero M, Maiorana E, Galbally J, Campisi P, Fierrez J. Multi-biometric template protection based on homomorphic encryption. Pattern Recognit. 2017;67:149\u201363.","journal-title":"Pattern Recognit"},{"key":"1733_CR107","doi-asserted-by":"publisher","first-page":"24273","DOI":"10.1109\/ACCESS.2022.3154826","volume":"10","author":"A Hassanpour","year":"2022","unstructured":"Hassanpour A, Moradikia M, Yang B, Abdelhadi A, Busch C, Fierrez J. Differential privacy preservation in robust continual learning. IEEE Access. 2022;10:24273\u20132428.","journal-title":"IEEE Access"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01733-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-023-01733-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01733-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T22:33:27Z","timestamp":1729550007000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-023-01733-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,7]]},"references-count":107,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["1733"],"URL":"https:\/\/doi.org\/10.1007\/s42979-023-01733-0","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,7]]},"assertion":[{"value":"10 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all the authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"434"}}