{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T13:35:56Z","timestamp":1773581756168,"version":"3.50.1"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030583088","type":"print"},{"value":"9783030583095","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58309-5_10","type":"book-chapter","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T10:03:17Z","timestamp":1598954597000},"page":"125-137","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["How (Not) to Measure Bias in Face Recognition Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7484-536X","authenticated-orcid":false,"given":"Stefan","family":"Gl\u00fcge","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0047-6802","authenticated-orcid":false,"given":"Mohammadreza","family":"Amirian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3699-4871","authenticated-orcid":false,"given":"Dandolo","family":"Flumini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3784-0420","authenticated-orcid":false,"given":"Thilo","family":"Stadelmann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,2]]},"reference":[{"key":"10_CR1","unstructured":"Adeli, E., et al.: Bias-resilient neural network. ArXiv abs\/1910.03676 (2019)"},{"key":"10_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1007\/978-3-030-11009-3_34","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"M Alvi","year":"2019","unstructured":"Alvi, M., Zisserman, A., Nell\u00e5ker, C.: Turning a blind eye: explicit removal of biases and variation from deep neural network embeddings. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 556\u2013572. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11009-3_34"},{"key":"10_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-319-99978-4_27","volume-title":"Artificial Neural Networks in Pattern Recognition","author":"M Amirian","year":"2018","unstructured":"Amirian, M., Schwenker, F., Stadelmann, T.: Trace and detect adversarial attacks on CNNs using feature response maps. In: Pancioni, L., Schwenker, F., Trentin, E. (eds.) ANNPR 2018. LNCS (LNAI), vol. 11081, pp. 346\u2013358. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99978-4_27"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Bellamy, R.K.E., et al.: AI fairness 360: an extensible toolkit for detecting and mitigating algorithmic bias. IBM J. Res. Dev. 63(4\/5), 1\u201315 (2019)","DOI":"10.1147\/JRD.2019.2942287"},{"key":"10_CR5","volume-title":"Dynamic Programming","author":"R Bellman","year":"1957","unstructured":"Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)"},{"issue":"2","key":"10_CR6","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1080\/23738871.2016.1228990","volume":"1","author":"P Bernal","year":"2016","unstructured":"Bernal, P.: Data gathering, surveillance and human rights: recasting the debate. J. Cyber Policy 1(2), 243\u2013264 (2016)","journal-title":"J. Cyber Policy"},{"key":"10_CR7","unstructured":"Brundage, M., Avin, S., Clark, J., Toner, H., et al.: The malicious use of artificial intelligence: forecasting, prevention, and mitigation. ArXiv abs\/1802.07228 (2019)"},{"key":"10_CR8","unstructured":"Buolamwini, J.A.: Gender shades: intersectional phenotypic and demographic evaluation of face datasets and gender classifiers. Master\u2019s thesis, MIT (2017)"},{"issue":"1","key":"10_CR9","first-page":"1","volume":"3","author":"T Cali\u0144ski","year":"1974","unstructured":"Cali\u0144ski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3(1), 1\u201327 (1974)","journal-title":"Commun. Stat."},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: VGGFace2: a dataset for recognising faces across pose and age. In: International Conference on Automatic Face Gesture Recognition, pp. 67\u201374 (2018)","DOI":"10.1109\/FG.2018.00020"},{"key":"10_CR11","unstructured":"Cavazos, J.G., Phillips, P.J., Castillo, C.D., O\u2019Toole, A.J.: Accuracy comparison across face recognition algorithms: where are we on measuring race bias? ArXiv abs\/1912.07398 (2019)"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1(2), 224\u2013227 (1979)","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: CVPR, pp. 4690\u20134699 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"issue":"1","key":"10_CR14","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1080\/01969727408546059","volume":"4","author":"JC Dunn","year":"1974","unstructured":"Dunn, J.C.: Well-separated clusters and optimal fuzzy partitions. J. Cybern. 4(1), 95\u2013104 (1974)","journal-title":"J. Cybern."},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Garcia, R.V., Wandzik, L., Grabner, L., Krueger, J.: The harms of demographic bias in deep face recognition research. In: ICB, pp. 1\u20136 (2019)","DOI":"10.1109\/ICB45273.2019.8987334"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.Z.: Xai\u2014explainable artificial intelligence. Sci. Robot. 4(37) (2019)","DOI":"10.1126\/scirobotics.aay7120"},{"key":"10_CR17","doi-asserted-by":"publisher","first-page":"102805","DOI":"10.1016\/j.cviu.2019.102805","volume":"189","author":"G Guo","year":"2019","unstructured":"Guo, G., Zhang, N.: A survey on deep learning based face recognition. Comput. Vis. Image Underst. 189, 102805 (2019)","journal-title":"Comput. Vis. Image Underst."},{"key":"10_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-319-46487-9_6","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Guo","year":"2016","unstructured":"Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87\u2013102. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_6"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Hannak, A., Soeller, G., Lazer, D., Mislove, A., Wilson, C.: Measuring price discrimination and steering on e-commerce web sites. In: Conference on Internet Measurement Conference, pp. 305\u2013318 (2014)","DOI":"10.1145\/2663716.2663744"},{"issue":"1","key":"10_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0282-4","volume":"7","author":"M Hashemi","year":"2020","unstructured":"Hashemi, M., Hall, M.: Criminal tendency detection from facial images and the gender bias effect. J. Big Data 7(1), 1\u201316 (2020). https:\/\/doi.org\/10.1186\/s40537-019-0282-4","journal-title":"J. Big Data"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Kortylewski, A., Egger, B., Schneider, A., Gerig, T., Morel-Forster, A., Vetter, T.: Analyzing and reducing the damage of dataset bias to face recognition with synthetic data. In: CVPR (2019)","DOI":"10.1109\/CVPRW.2019.00279"},{"key":"10_CR23","unstructured":"Learned-Miller, E., Ord\u00f3\u00f1ez, V., Morgenster, J., Buolamwini, J.: Facial recognition technologies in the wild: a call for a federal office. Technical report, Algorithmic Justice League, May 2020"},{"key":"10_CR24","unstructured":"Leg\u00e1ny, C., Juh\u00e1sz, S., Babos, A.: Cluster validity measurement techniques. In: International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, pp. 388\u2013393 (2006)"},{"key":"10_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-85729-932-1","volume-title":"Handbook of Face Recognition","author":"S Li","year":"2011","unstructured":"Li, S., Jain, A.: Handbook of Face Recognition. Springer, London (2011). https:\/\/doi.org\/10.1007\/978-0-85729-932-1"},{"key":"10_CR26","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.patcog.2017.10.015","volume":"75","author":"Y Li","year":"2018","unstructured":"Li, Y., Wang, G., Nie, L., Wang, Q., Tan, W.: Distance metric optimization driven convolutional neural network for age invariant face recognition. Pattern Recognit. 75, 51\u201362 (2018)","journal-title":"Pattern Recognit."},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J.: Understanding of internal clustering validation measures. In: International Conference on Data Mining, pp. 911\u2013916 (2010)","DOI":"10.1109\/ICDM.2010.35"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Loi, M., Heitz, C., Christen, M.: A comparative assessment and synthesis of twenty ethics codes on AI and big data. In: Swiss Conference on Data Science (2020)","DOI":"10.1109\/SDS49233.2020.00015"},{"key":"10_CR29","first-page":"121","volume":"40","author":"M Mann","year":"2017","unstructured":"Mann, M., Smith, M.: Automated facial recognition technology: recent developments and approaches to oversight. Univ. N. S. W. Law J. 40, 121\u2013145 (2017)","journal-title":"Univ. N. S. W. Law J."},{"key":"10_CR30","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ArXiv abs\/1908.09635 (2019)"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Meissner, C.A., Brigham, J.C.: Thirty years of investigating the own-race bias in memory for faces: a meta-analytic review. Psychol. Public Policy Law, 3\u201335 (2001)","DOI":"10.1037\/\/1076-8971.7.1.3"},{"issue":"5","key":"10_CR32","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1109\/TMM.2018.2876046","volume":"21","author":"M Merler","year":"2019","unstructured":"Merler, M., Mac, K.N.C., Joshi, D., et al.: Automatic curation of sports highlights using multimodal excitement features. IEEE Trans. Multimed. 21(5), 1147\u20131160 (2019)","journal-title":"IEEE Trans. Multimed."},{"key":"10_CR33","unstructured":"Merler, M., Ratha, N.K., Feris, R.S., Smith, J.R.: Diversity in faces. ArXiv abs\/1901.10436 (2019)"},{"issue":"4","key":"10_CR34","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1177\/1461444816688896","volume":"19","author":"A Norval","year":"2017","unstructured":"Norval, A., Prasopoulou, E.: Public faces? A critical exploration of the diffusion of face recognition technologies in online social networks. New Media Soc. 19(4), 637\u2013654 (2017)","journal-title":"New Media Soc."},{"key":"10_CR35","first-page":"1","volume":"11","author":"DJ Robertson","year":"2016","unstructured":"Robertson, D.J., Noyes, E., Dowsett, A.J., Jenkins, R., Burton, A.M.: Face recognition by metropolitan police super-recognisers. PloS ONE 11, 1\u20138 (2016)","journal-title":"PloS ONE"},{"key":"10_CR36","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? ArXiv abs\/2002.06483 (2020)","DOI":"10.1109\/CVPRW50498.2020.00008"},{"issue":"2\u20134","key":"10_CR37","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/s11263-016-0940-3","volume":"126","author":"R Rothe","year":"2018","unstructured":"Rothe, R., Timofte, R., Gool, L.V.: Deep expectation of real and apparent age from a single image without facial landmarks. Int. J. Comput. Vis. 126(2\u20134), 144\u2013157 (2018)","journal-title":"Int. J. Comput. Vis."},{"key":"10_CR38","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987)","journal-title":"J. Comput. Appl. Math."},{"issue":"2","key":"10_CR39","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s10676-018-9452-x","volume":"20","author":"L Royakkers","year":"2018","unstructured":"Royakkers, L., Timmer, J., Kool, L., van Est, R.: Societal and ethical issues of digitization. Ethics Inf. Technol. 20(2), 127\u2013142 (2018)","journal-title":"Ethics Inf. Technol."},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"10_CR41","unstructured":"Serna, I., Pe\u00f1a, A., Morales, A., Fierrez, J.: InsideBias: measuring bias in deep networks and application to face gender biometrics. ArXiv abs\/2004.06592 (2020)"},{"issue":"4","key":"10_CR42","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1109\/TIFS.2015.2398819","volume":"10","author":"DF Smith","year":"2015","unstructured":"Smith, D.F., Wiliem, A., Lovell, B.C.: Face recognition on consumer devices: reflections on replay attacks. IEEE Trans. Inf. Forensics Secur. 10(4), 736\u2013745 (2015)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10_CR43","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-319-99978-4_2","volume-title":"Artificial Neural Networks in Pattern Recognition","author":"T Stadelmann","year":"2018","unstructured":"Stadelmann, T., et al.: Deep learning in the wild. In: Pancioni, L., Schwenker, F., Trentin, E. (eds.) ANNPR 2018. LNCS (LNAI), vol. 11081, pp. 17\u201338. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99978-4_2"},{"key":"10_CR44","unstructured":"Steed, R., Caliskan, A.: Machines learn appearance bias in face recognition. ArXiv abs\/2002.05636 (2020)"},{"key":"10_CR45","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: ICCV, pp. 692\u2013702 (2019)","DOI":"10.1109\/ICCV.2019.00078"},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Wang, T., Lin, X.V., Rajani, N.F., McCann, B., et al.: Double-hard debias: tailoring word embeddings for gender bias mitigation. ArXiv abs\/2005.00965 (2020)","DOI":"10.18653\/v1\/2020.acl-main.484"},{"key":"10_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-030-01231-1_5","volume-title":"Computer Vision \u2013 ECCV 2018","author":"B Yu","year":"2018","unstructured":"Yu, B., Liu, T., Gong, M., Ding, C., Tao, D.: Correcting the triplet selection bias for triplet loss. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 71\u201386. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_5. Please check and confirm the edit made in Ref. [47]."},{"issue":"10","key":"10_CR48","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499\u20131503 (2016)","journal-title":"IEEE Signal Process. Lett."},{"key":"10_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Song, Y., Qi, H.: Age progression\/regression by conditional adversarial autoencoder. In: CVPR, pp. 4352\u20134360 (2017)","DOI":"10.1109\/CVPR.2017.463"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks in Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58309-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:04:41Z","timestamp":1725149081000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58309-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030583088","9783030583095"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58309-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"2 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ANNPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IAPR Workshop on Artificial Neural Networks in Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Winterthur","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"annpr2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/annpr2020.ch\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was helt virtually due to COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}