{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T03:00:12Z","timestamp":1791342012281,"version":"4.3.1"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031442667","type":"print"},{"value":"9783031442674","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44267-4_15","type":"book-chapter","created":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:03:27Z","timestamp":1696118607000},"page":"291-311","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Monitoring Algorithmic Fairness Under Partial Observations"],"prefix":"10.1007","author":[{"given":"Thomas A.","family":"Henzinger","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantin","family":"Kueffner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaushik","family":"Mallik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"issue":"1","key":"15_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3158668","volume":"28","author":"G Agha","year":"2018","unstructured":"Agha, G., Palmskog, K.: A survey of statistical model checking. ACM Trans. Model. Comput. Simul. (TOMACS) 28(1), 1\u201339 (2018)","journal-title":"ACM Trans. Model. Comput. Simul. (TOMACS)"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Albarghouthi, A., D\u2019Antoni, L., Drews, S., Nori, A.V.: FairSquare: probabilistic verification of program fairness. Proc. ACM Program. Lang. 1(OOPSLA), 1\u201330 (2017)","DOI":"10.1145\/3133904"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Albarghouthi, A., Vinitsky, S.: Fairness-aware programming. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 211\u2013219 (2019)","DOI":"10.1145\/3287560.3287588"},{"issue":"6","key":"15_CR4","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1109\/TSE.2003.1205180","volume":"29","author":"C Baier","year":"2003","unstructured":"Baier, C., Haverkort, B., Hermanns, H., Katoen, J.P.: Model-checking algorithms for continuous-time Markov chains. IEEE Trans. Softw. Eng. 29(6), 524\u2013541 (2003). https:\/\/doi.org\/10.1109\/TSE.2003.1205180","journal-title":"IEEE Trans. Softw. Eng."},{"key":"15_CR5","volume-title":"Principles of Model Checking","author":"C Baier","year":"2008","unstructured":"Baier, C., Katoen, J.P.: Principles of Model Checking. MIT Press, Cambridge (2008)"},{"key":"15_CR6","unstructured":"Balunovic, M., Ruoss, A., Vechev, M.: Fair normalizing flows. In: International Conference on Learning Representations (2021)"},{"key":"15_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/978-3-319-75632-5_5","volume-title":"Lectures on Runtime Verification","author":"E Bartocci","year":"2018","unstructured":"Bartocci, E., et al.: Specification-based monitoring of cyber-physical systems: a survey on theory, tools and applications. In: Bartocci, E., Falcone, Y. (eds.) Lectures on Runtime Verification. LNCS, vol. 10457, pp. 135\u2013175. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75632-5_5"},{"key":"15_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-75632-5","volume-title":"Lectures on Runtime Verification","author":"E Bartocci","year":"2018","unstructured":"Bartocci, E., Falcone, Y.: Lectures on Runtime Verification. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75632-5"},{"key":"15_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/978-3-030-78142-2_7","volume-title":"Coordination Models and Languages","author":"C Bartolo Burl\u00f2","year":"2021","unstructured":"Bartolo Burl\u00f2, C., Francalanza, A., Scalas, A., Trubiani, C., Tuosto, E.: Towards probabilistic session-type monitoring. In: Damiani, F., Dardha, O. (eds.) COORDINATION 2021. LNCS, vol. 12717, pp. 106\u2013120. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78142-2_7"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Bellamy, R.K., et al.: AI fairness 360: an extensible toolkit for detecting and mitigating algorithmic bias. IBM J. Res. Dev. 63(4\/5), 4\u20131 (2019)","DOI":"10.1147\/JRD.2019.2942287"},{"key":"15_CR11","unstructured":"Bird, S., et al.: Fairlearn: a toolkit for assessing and improving fairness in AI. Microsoft, Technical report. MSR-TR-2020-32 (2020)"},{"issue":"2","key":"15_CR12","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1089\/big.2016.0047","volume":"5","author":"A Chouldechova","year":"2017","unstructured":"Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153\u2013163 (2017)","journal-title":"Big Data"},{"key":"15_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-24372-1_1","volume-title":"Automated Technology for Verification and Analysis","author":"EM Clarke","year":"2011","unstructured":"Clarke, E.M., Zuliani, P.: Statistical model checking for cyber-physical systems. In: Bultan, T., Hsiung, P.-A. (eds.) ATVA 2011. LNCS, vol. 6996, pp. 1\u201312. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-24372-1_1"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"D\u2019Amour, A., Srinivasan, H., Atwood, J., Baljekar, P., Sculley, D., Halpern, Y.: Fairness is not static: deeper understanding of long term fairness via simulation studies. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* 2020, pp. 525\u2013534 (2020)","DOI":"10.1145\/3351095.3372878"},{"key":"15_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1007\/978-3-642-38088-4_24","volume-title":"NASA Formal Methods","author":"A David","year":"2013","unstructured":"David, A., Du, D., Guldstrand Larsen, K., Legay, A., Miku\u010dionis, M.: Optimizing control strategy using statistical model checking. In: Brat, G., Rungta, N., Venet, A. (eds.) NFM 2013. LNCS, vol. 7871, pp. 352\u2013367. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38088-4_24"},{"key":"15_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-642-15297-9_9","volume-title":"Formal Modeling and Analysis of Timed Systems","author":"A Donz\u00e9","year":"2010","unstructured":"Donz\u00e9, A., Maler, O.: Robust satisfaction of temporal logic over real-valued signals. In: Chatterjee, K., Henzinger, T.A. (eds.) FORMATS 2010. LNCS, vol. 6246, pp. 92\u2013106. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15297-9_9"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Dressel, J., Farid, H.: The accuracy, fairness, and limits of predicting recidivism. Sci. Adv. 4(1), eaao5580 (2018)","DOI":"10.1126\/sciadv.aao5580"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214\u2013226 (2012)","DOI":"10.1145\/2090236.2090255"},{"key":"15_CR19","unstructured":"Ensign, D., Friedler, S.A., Neville, S., Scheidegger, C., Venkatasubramanian, S.: Runaway feedback loops in predictive policing. In: Conference on Fairness, Accountability and Transparency, pp. 160\u2013171. PMLR (2018)"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Esposito, A.R., Mondelli, M.: Concentration without independence via information measures. arXiv preprint arXiv:2303.07245 (2023)","DOI":"10.1109\/ISIT54713.2023.10206899"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Feldman, M., Friedler, S.A., Moeller, J., Scheidegger, C., Venkatasubramanian, S.: Certifying and removing disparate impact. In: proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 259\u2013268 (2015)","DOI":"10.1145\/2783258.2783311"},{"key":"15_CR22","unstructured":"Ferrere, T., Henzinger, T.A., Kragl, B.: Monitoring event frequencies. In: 28th EACSL Annual Conference on Computer Science Logic, vol. 152 (2020)"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Ferr\u00e8re, T., Henzinger, T.A., Sara\u00e7, N.E.: A theory of register monitors. In: Proceedings of the 33rd Annual ACM\/IEEE Symposium on Logic in Computer Science, pp. 394\u2013403 (2018)","DOI":"10.1145\/3209108.3209194"},{"issue":"4","key":"15_CR24","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/S1571-0661(04)80576-0","volume":"70","author":"B Finkbeiner","year":"2002","unstructured":"Finkbeiner, B., Sankaranarayanan, S., Sipma, H.: Collecting statistics over runtime executions. Electron. Notes Theor. Comput. Sci. 70(4), 36\u201354 (2002)","journal-title":"Electron. Notes Theor. Comput. Sci."},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Ghosh, B., Basu, D., Meel, K.S.: Justicia: a stochastic sat approach to formally verify fairness. arXiv preprint arXiv:2009.06516 (2020)","DOI":"10.1609\/aaai.v35i9.16925"},{"key":"15_CR26","unstructured":"Ghosh, B., Basu, D., Meel, K.S.: Algorithmic fairness verification with graphical models. arXiv preprint arXiv:2109.09447 (2021)"},{"key":"15_CR27","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Henzinger, T., Karimi, M., Kueffner, K., Mallik, K.: Runtime monitoring of dynamic fairness properties. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 604\u2013614 (2023)","DOI":"10.1145\/3593013.3594028"},{"key":"15_CR29","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/978-3-031-37703-7_17","volume-title":"Computer Aided Verification","author":"TA Henzinger","year":"2023","unstructured":"Henzinger, T.A., Karimi, M., Kueffner, K., Mallik, K.: Monitoring algorithmic fairness. In: Enea, C., Lal, A. (eds.) Computer Aided Verification, pp. 358\u2013382. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-37703-7_17"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Henzinger, T.A., Kueffner, K., Mallik, K.: Monitoring algorithmic fairness under partial observations. arXiv preprint arXiv:2308.00341 (2023)","DOI":"10.1007\/978-3-031-37703-7_17"},{"key":"15_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-60508-7_1","volume-title":"Runtime Verification","author":"TA Henzinger","year":"2020","unstructured":"Henzinger, T.A., Sara\u00e7, N.E.: Monitorability under assumptions. In: Deshmukh, J., Ni\u010dkovi\u0107, D. (eds.) RV 2020. LNCS, vol. 12399, pp. 3\u201318. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60508-7_1"},{"key":"15_CR32","doi-asserted-by":"crossref","unstructured":"Henzinger, T.A., Sara\u00e7, N.E.: Quantitative and approximate monitoring. In: 2021 36th Annual ACM\/IEEE Symposium on Logic in Computer Science (LICS), pp. 1\u201314. IEEE (2021)","DOI":"10.1109\/LICS52264.2021.9470547"},{"key":"15_CR33","unstructured":"Jerison, D.: General mixing time bounds for finite Markov chains via the absolute spectral gap. arXiv preprint arXiv:1310.8021 (2013)"},{"key":"15_CR34","unstructured":"John, P.G., Vijaykeerthy, D., Saha, D.: Verifying individual fairness in machine learning models. In: Conference on Uncertainty in Artificial Intelligence, pp. 749\u2013758. PMLR (2020)"},{"key":"15_CR35","series-title":"The IMA Volumes in Mathematics and its Applications","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/978-1-4939-7005-6_6","volume-title":"Convexity and Concentration","author":"A Kontorovich","year":"2017","unstructured":"Kontorovich, A., Raginsky, M.: Concentration of measure without independence: a unified approach via the martingale method. In: Carlen, E., Madiman, M., Werner, E.M. (eds.) Convexity and Concentration. TIVMA, vol. 161, pp. 183\u2013210. Springer, New York (2017). https:\/\/doi.org\/10.1007\/978-1-4939-7005-6_6"},{"key":"15_CR36","doi-asserted-by":"crossref","unstructured":"Lahoti, P., Gummadi, K.P., Weikum, G.: iFair: learning individually fair data representations for algorithmic decision making. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1334\u20131345. IEEE (2019)","DOI":"10.1109\/ICDE.2019.00121"},{"key":"15_CR37","doi-asserted-by":"crossref","unstructured":"Levin, D.A., Peres, Y.: Markov Chains and Mixing Times, vol. 107. American Mathematical Society (2017)","DOI":"10.1090\/mbk\/107"},{"key":"15_CR38","doi-asserted-by":"crossref","unstructured":"Liu, L.T., Dean, S., Rolf, E., Simchowitz, M., Hardt, M.: Delayed impact of fair machine learning. In: International Conference on Machine Learning, pp. 3150\u20133158. PMLR (2018)","DOI":"10.24963\/ijcai.2019\/862"},{"issue":"5","key":"15_CR39","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1111\/j.1740-9713.2016.00960.x","volume":"13","author":"K Lum","year":"2016","unstructured":"Lum, K., Isaac, W.: To predict and serve? Significance 13(5), 14\u201319 (2016)","journal-title":"Significance"},{"key":"15_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/978-3-540-30206-3_12","volume-title":"Formal Techniques, Modelling and Analysis of Timed and Fault-Tolerant Systems","author":"O Maler","year":"2004","unstructured":"Maler, O., Nickovic, D.: Monitoring temporal properties of continuous signals. In: Lakhnech, Y., Yovine, S. (eds.) FORMATS\/FTRTFT -2004. LNCS, vol. 3253, pp. 152\u2013166. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-30206-3_12"},{"key":"15_CR41","unstructured":"Meyer, A., Albarghouthi, A., D\u2019Antoni, L.: Certifying robustness to programmable data bias in decision trees. In: Advances in Neural Information Processing Systems, vol. 34, 26276\u201326288 (2021)"},{"issue":"6464","key":"15_CR42","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","volume":"366","author":"Z Obermeyer","year":"2019","unstructured":"Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S.: Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464), 447\u2013453 (2019)","journal-title":"Science"},{"key":"15_CR43","unstructured":"Otop, J., Henzinger, T.A., Chatterjee, K.: Quantitative automata under probabilistic semantics. Logical Methods Comput. Sci. 15 (2019)"},{"key":"15_CR44","doi-asserted-by":"crossref","unstructured":"Paulin, D.: Concentration inequalities for Markov chains by Marton couplings and spectral methods (2015)","DOI":"10.1214\/EJP.v20-4039"},{"issue":"11","key":"15_CR45","doi-asserted-by":"publisher","first-page":"3288","DOI":"10.1109\/TCAD.2020.3012643","volume":"39","author":"I Ruchkin","year":"2020","unstructured":"Ruchkin, I., Sokolsky, O., Weimer, J., Hedaoo, T., Lee, I.: Compositional probabilistic analysis of temporal properties over stochastic detectors. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 39(11), 3288\u20133299 (2020)","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"key":"15_CR46","doi-asserted-by":"crossref","unstructured":"Scheuerman, M.K., Paul, J.M., Brubaker, J.R.: How computers see gender: an evaluation of gender classification in commercial facial analysis services. Proc. ACM Hum.-Comput. Interact. 3(CSCW), 1\u201333 (2019)","DOI":"10.1145\/3359246"},{"key":"15_CR47","unstructured":"Sharifi-Malvajerdi, S., Kearns, M., Roth, A.: Average individual fairness: algorithms, generalization and experiments. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"15_CR48","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-642-29860-8_15","volume-title":"Runtime Verification","author":"SD Stoller","year":"2012","unstructured":"Stoller, S.D., et al.: Runtime verification with state estimation. In: Khurshid, S., Sen, K. (eds.) RV 2011. LNCS, vol. 7186, pp. 193\u2013207. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-29860-8_15"},{"key":"15_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-3-030-90870-6_5","volume-title":"Formal Methods","author":"B Sun","year":"2021","unstructured":"Sun, B., Sun, J., Dai, T., Zhang, L.: Probabilistic verification of neural networks against group fairness. In: Huisman, M., P\u0103s\u0103reanu, C., Zhan, N. (eds.) FM 2021. LNCS, vol. 13047, pp. 83\u2013102. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-90870-6_5"},{"key":"15_CR50","unstructured":"Waudby-Smith, I., Arbour, D., Sinha, R., Kennedy, E.H., Ramdas, A.: Time-uniform central limit theory, asymptotic confidence sequences, and anytime-valid causal inference. arXiv preprint arXiv:2103.06476 (2021)"},{"issue":"1","key":"15_CR51","first-page":"56","volume":"26","author":"J Wexler","year":"2019","unstructured":"Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Vi\u00e9gas, F., Wilson, J.: The what-if tool: interactive probing of machine learning models. IEEE Trans. Vis. Comput. Graph. 26(1), 56\u201365 (2019)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"15_CR52","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/3-540-45657-0_17","volume-title":"Computer Aided Verification","author":"HLS Younes","year":"2002","unstructured":"Younes, H.L.S., Simmons, R.G.: Probabilistic verification of discrete event systems using acceptance sampling. In: Brinksma, E., Larsen, K.G. (eds.) CAV 2002. LNCS, vol. 2404, pp. 223\u2013235. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-45657-0_17"},{"key":"15_CR53","unstructured":"Zemel, R., Wu, Y., Swersky, K., Pitassi, T., Dwork, C.: Learning fair representations. In: International Conference on Machine Learning, pp. 325\u2013333. PMLR (2013)"}],"container-title":["Lecture Notes in Computer Science","Runtime Verification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44267-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:14:32Z","timestamp":1696119272000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44267-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442667","9783031442674"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44267-4_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Runtime Verification","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thessaloniki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rv23.csd.auth.gr","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":"39","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":"13","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":"7","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":"33% - 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":"3,15","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The proceedings includes also 4 Tutorial Papers and  2 Invited Papers","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)"}}]}}