{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T03:34:20Z","timestamp":1769830460677,"version":"3.49.0"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032054340","type":"print"},{"value":"9783032054357","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-05435-7_1","type":"book-chapter","created":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T01:30:13Z","timestamp":1757727013000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Algorithmic Fairness: A Runtime Perspective"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0783-904X","authenticated-orcid":false,"given":"Filip","family":"Cano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2985-7724","authenticated-orcid":false,"given":"Thomas A.","family":"Henzinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8974-2542","authenticated-orcid":false,"given":"Konstantin","family":"Kueffner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"key":"1_CR1","unstructured":"Alamdari, P.A., Klassen, T.Q., Creager, E., Mcilraith, S.A.: Remembering to be fair: non-Markovian fairness in sequential decision making. In: Proceedings of the International Conference on Machine Learning (ICML), vol.\u00a0235, pp. 906\u2013920. PMLR (2024)"},{"issue":"OOPSLA","key":"1_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3133904","volume":"1","author":"A Albarghouthi","year":"2017","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)","journal-title":"Proc. ACM Program. Lang."},{"key":"1_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":"1_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":"1_CR5","unstructured":"Balunovic, M., Ruoss, A., Vechev, M.: Fair normalizing flows. In: International Conference on Learning Representations (2021)"},{"key":"1_CR6","unstructured":"Barocas, S., Hardt, M., Narayanan, A.: Fairness and Machine Learning Limitations and Opportunities. MIT Press (2023)"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Bartocci, E., et al.: Specification-based monitoring of cyber-physical systems: a survey on theory, tools and applications. In: Lectures on Runtime Verification, pp. 135\u2013175. Springer (2018)","DOI":"10.1007\/978-3-319-75632-5_5"},{"issue":"OOPSLA","key":"1_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3360544","volume":"3","author":"O Bastani","year":"2019","unstructured":"Bastani, O., Zhang, X., Solar-Lezama, A.: Probabilistic verification of fairness properties via concentration. Proc. ACM Program. Lang. 3(OOPSLA), 1\u201327 (2019)","journal-title":"Proc. ACM Program. Lang."},{"issue":"4\/5","key":"1_CR9","doi-asserted-by":"publisher","first-page":"4:1","DOI":"10.1147\/JRD.2019.2942287","volume":"63","author":"RK Bellamy","year":"2019","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), 4:1-4:15 (2019)","journal-title":"IBM J. Res. Dev."},{"issue":"1","key":"1_CR10","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/0049124118782533","volume":"50","author":"R Berk","year":"2021","unstructured":"Berk, R., Heidari, H., Jabbari, S., Kearns, M., Roth, A.: Fairness in criminal justice risk assessments: the state of the art. Sociol. Methods Res. 50(1), 3\u201344 (2021)","journal-title":"Sociol. Methods Res."},{"key":"1_CR11","unstructured":"Bird, S., et al.: FairLearn: a toolkit for assessing and improving fairness in AI. Microsoft, Technical Report, MSR-TR-2020-32 (2020)"},{"key":"1_CR12","unstructured":"Cano, F., Henzinger, T.A., K\u00f6nighofer, B., Kueffner, K., Mallik, K.: Abstraction-based decision making for statistical properties. In: International Conference on Formal Structures for Computation and Deduction (FSCD). LIPIcs, vol.\u00a0299, pp. 2:1\u20132:17. Schloss Dagstuhl - Leibniz-Zentrum f\u00fcr Informatik (2024)"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Cano, F., Henzinger, T.A., Kueffner, K.: Algorithmic fairness: a runtime perspective. arXiv preprint arXiv:2507.20711 (2025)","DOI":"10.1007\/978-3-032-05435-7_1"},{"issue":"15","key":"1_CR14","first-page":"15659","volume":"39","author":"F Cano","year":"2025","unstructured":"Cano, F., Henzinger, T.A., K\u00f6nighofer, B., Kueffner, K., Mallik, K.: Fairness shields: safeguarding against biased decision makers. Proc. AAAI Conf. Artif. Intell. 39(15), 15659\u201315668 (2025)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1_CR15","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 Conference on Fairness, Accountability, and Transparency (FAccT), pp. 525\u2013534 (2020)","DOI":"10.1145\/3351095.3372878"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Donz\u00e9, A., Maler, O.: Robust satisfaction of temporal logic over real-valued signals. In: International Conference on Formal Modeling and Analysis of Timed Systems, pp. 92\u2013106. Springer (2010)","DOI":"10.1007\/978-3-642-15297-9_9"},{"issue":"1","key":"1_CR17","doi-asserted-by":"publisher","first-page":"eaao5580","DOI":"10.1126\/sciadv.aao5580","volume":"4","author":"J Dressel","year":"2018","unstructured":"Dressel, J., Farid, H.: The accuracy, fairness, and limits of predicting recidivism. Sci. Adv. 4(1), eaao5580 (2018)","journal-title":"Sci. Adv."},{"key":"1_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 (ITCS), pp. 214\u2013226. ACM, New York, NY, USA (2012)","DOI":"10.1145\/2090236.2090255"},{"key":"1_CR19","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":"1_CR20","unstructured":"Faymonville, P., Finkbeiner, B., Schwenger, M., Torfah, H.: Real-time stream-based monitoring. arXiv preprint arXiv:1711.03829 (2017)"},{"key":"1_CR21","unstructured":"Ghosh, B., Basu, D., Meel, K.S.: Algorithmic fairness verification with graphical models. arXiv preprint arXiv:2109.09447 (2021)"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Ghosh, B., Basu, D., Meel, K.S.: Justicia: a stochastic sat approach to formally verify fairness. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol.\u00a035, pp. 7554\u20137563 (2021)","DOI":"10.1609\/aaai.v35i9.16925"},{"key":"1_CR23","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 3315\u20133323 (2016)"},{"key":"1_CR24","unstructured":"Heidari, H., Nanda, V., Gummadi, K.P.: On the long-term impact of algorithmic decision policies: effort unfairness and feature segregation through social learning. arXiv preprint arXiv:1903.01209 (2019)"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Henzinger, T.A., Karimi, M., Kueffner, K., Mallik, K.: Monitoring algorithmic fairness. In: Proceedings of the International Computer Aided Verification (CAV), pp. 358\u2013382. Springer, Verlag (2023)","DOI":"10.1007\/978-3-031-37703-7_17"},{"key":"1_CR26","doi-asserted-by":"publisher","unstructured":"Henzinger, T.A., Karimi, M., Kueffner, K., Mallik, K.: Runtime monitoring of dynamic fairness properties. In: Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), pp. 604\u2013614. ACM (2023). https:\/\/doi.org\/10.1145\/3593013.3594028","DOI":"10.1145\/3593013.3594028"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Henzinger, T.A., Kueffner, K., Mallik, K.: Monitoring algorithmic fairness under partial observations. In: International Conference on Runtime Verification (RV), pp. 291\u2013311. Springer (2023)","DOI":"10.1007\/978-3-031-44267-4_15"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Henzinger, T.A., Kueffner, K., Mallik, K.: Monitoring static fairness. arXiv preprint arXiv:2507.03048 (2025)","DOI":"10.21203\/rs.3.rs-7074584\/v1"},{"issue":"301","key":"1_CR29","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1080\/01621459.1963.10500830","volume":"58","author":"W Hoeffding","year":"1963","unstructured":"Hoeffding, W.: Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58(301), 13\u201330 (1963)","journal-title":"J. Am. Stat. Assoc."},{"issue":"2","key":"1_CR30","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1214\/20-AOS1991","volume":"49","author":"SR Howard","year":"2021","unstructured":"Howard, S.R., Ramdas, A., McAuliffe, J., Sekhon, J.: Time-uniform, nonparametric, nonasymptotic confidence sequences. Ann. Stat. 49(2), 1055\u20131080 (2021)","journal-title":"Ann. Stat."},{"key":"1_CR31","unstructured":"Jagielski, M., et al.: Differentially private fair learning. In: International Conference on Machine Learning, pp. 3000\u20133008. PMLR (2019)"},{"key":"1_CR32","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":"1_CR33","doi-asserted-by":"crossref","unstructured":"Junges, S., Torfah, H., Seshia, S.A.: Runtime monitors for Markov decision processes. In: International Conference on Computer Aided Verification, pp. 553\u2013576. Springer (2021)","DOI":"10.1007\/978-3-030-81688-9_26"},{"key":"1_CR34","unstructured":"Kearns, M., Neel, S., Roth, A., Wu, Z.S.: Preventing fairness gerrymandering: auditing and learning for subgroup fairness. In: International Conference on Machine Learning, pp. 2564\u20132572. PMLR (2018)"},{"key":"1_CR35","unstructured":"Konstantinov, N.H., Lampert, C.: Fairness-aware PAC learning from corrupted data. JMLR 23 (2022)"},{"key":"1_CR36","unstructured":"Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"1_CR37","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"},{"key":"1_CR38","doi-asserted-by":"crossref","unstructured":"Maler, O., Nickovic, D.: Monitoring temporal properties of continuous signals. In: International Symposium on Formal Techniques in Real-Time and Fault-Tolerant Systems, pp. 152\u2013166. Springer (2004)","DOI":"10.1007\/978-3-540-30206-3_12"},{"issue":"6","key":"1_CR39","first-page":"1","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM CSUR 54(6), 1\u201335 (2021)","journal-title":"ACM CSUR"},{"key":"1_CR40","first-page":"26276","volume":"34","author":"A Meyer","year":"2021","unstructured":"Meyer, A., Albarghouthi, A., D\u2019Antoni, L.: Certifying robustness to programmable data bias in decision trees. Adv. Neural Inf. Process. Syst. (NeurIPS) 34, 26276\u201326288 (2021)","journal-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"issue":"6464","key":"1_CR41","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"},{"issue":"CSCW","key":"1_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3359246","volume":"3","author":"MK Scheuerman","year":"2019","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)","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"1_CR43","unstructured":"Sharifi-Malvajerdi, S., Kearns, M., Roth, A.: Average individual fairness: algorithms, generalization and experiments. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"1_CR44","doi-asserted-by":"crossref","unstructured":"Stoller, S.D., et al.: Runtime verification with state estimation. In: International Conference on Runtime Verification, pp. 193\u2013207. Springer (2011)","DOI":"10.1007\/978-3-642-29860-8_15"},{"key":"1_CR45","doi-asserted-by":"crossref","unstructured":"Sun, B., Sun, J., Dai, T., Zhang, L.: Probabilistic verification of neural networks against group fairness. In: International Symposium on Formal Methods, pp. 83\u2013102. Springer (2021)","DOI":"10.1007\/978-3-030-90870-6_5"},{"issue":"1","key":"1_CR46","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. Visual Comput. Graph. 26(1), 56\u201365 (2019)","journal-title":"IEEE Trans. Visual Comput. Graph."},{"key":"1_CR47","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\/978-3-032-05435-7_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T07:43:24Z","timestamp":1766043804000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05435-7_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,12]]},"ISBN":["9783032054340","9783032054357"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05435-7_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,12]]},"assertion":[{"value":"12 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that\u00a0are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Graz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rv25.isec.tugraz.at\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}