{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T06:34:58Z","timestamp":1782801298601,"version":"3.54.5"},"reference-count":76,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"vor","delay-in-days":2,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["AI Magazine"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Proportional representation is a foundational principle in social choice theory, ensuring that groups influence collective decisions in proportion to their size. While it has traditionally been studied in the context of political elections, recent work in computational social choice has broadened its scope to a variety of voting frameworks. This article showcases how proportional representation can be formalized and applied beyond these frameworks, spotlighting AI domains where it naturally takes shape. In particular, we focus on two such domains: clustering and AI alignment. In clustering, proportionality ensures that sufficiently large and cohesive groups of data points or agents are adequately represented in the selection of cluster centers or group assignments, to both centroid\u2010based and noncentroid\u2010based paradigms. In AI alignment, particularly in reinforcement learning from human feedback (RLHF), proportionality provides a principled framework for aggregating heterogeneous preferences by designing committees of reward functions that reflect annotators' viewpoints in proportion to their prevalence. We also discuss additional promising applications, including client selection in federated learning and forming committees of pre\u2010trained models in meta\u2010learning, and argue that incorporating proportional representation into AI systems provides a mathematically rigorous foundation for aligning algorithmic outcomes with the breadth of human\u00a0viewpoints.<\/jats:p>","DOI":"10.1002\/aaai.70044","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T09:16:41Z","timestamp":1764753401000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["How Proportional Representation Can Shape Artificial Intelligence"],"prefix":"10.1002","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7373-5898","authenticated-orcid":false,"given":"Evi","family":"Micha","sequence":"first","affiliation":[{"name":"Thomas Lord Deparment of Computer Science University of Southern California Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5176-9"},{"key":"e_1_2_8_3_1","article-title":"Foundational challenges in assuring alignment and safety of large language models","author":"Anwar U.","year":"2024","journal-title":"Transactions on Machine Learning Research"},{"key":"e_1_2_8_4_1","volume-title":"Social Choice and Individual Values","author":"Arrow K. J.","year":"1964"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00355-016-1019-3"},{"key":"e_1_2_8_6_1","unstructured":"Aziz H. B. E.Lee S.Morota Chu andJ.Vollen.2024. \u201cProportionally representative clustering.\u201d InProceedings of the 20th Conference on Web and Internet Economics (WINE) 1075\u20131083."},{"key":"e_1_2_8_7_1","unstructured":"Balcan M.\u2010F. T.Dick R.Noothigattu andA. D.Procaccia.2019. \u201cEnvy\u2010free classification.\u201d InProceedings of the 33rd Annual Conference on Neural Information Processing Systems (NeurIPS) 1238\u20131248."},{"key":"e_1_2_8_8_1","volume-title":"Fair representation: Meeting the Ideal of One Man, One Vote","author":"Balinski M. L.","year":"2010"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1177\/02783649231208729"},{"key":"e_1_2_8_10_1","unstructured":"Biyik E. andD.Sadigh.2018. \u201cBatch active preference\u2010based learning of reward functions.\u201d InConference on Robot Learning 519\u2013528.PMLR."},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107446984"},{"key":"e_1_2_8_12_1","unstructured":"Caragiannis I. E.Micha andJ.Peters.2024. \u201cCan a few decide for many? the metric distortion of sortition.\u201d InProceedings of the 41st International Conference on Machine Learning (ICML)."},{"key":"e_1_2_8_13_1","doi-asserted-by":"crossref","unstructured":"Caragiannis I. E.Micha andN.Shah.2024. \u201cProportional fairness in non\u2010centroid clustering.\u201d InProceedings of the 41st International Conference on Machine Learning (ICML).","DOI":"10.52202\/079017-0605"},{"key":"e_1_2_8_14_1","unstructured":"Chakraborty S. J.Qiu etal.2024. \u201cMaxMin\u2010RLHF: Alignment with diverse human preferences.\u201d InProceedings of the 41st International Conference on Machine Learning volume235 6116\u20136135."},{"key":"e_1_2_8_15_1","doi-asserted-by":"crossref","unstructured":"Chen A. S.Malladi L. H.Zhang X.Chen Q.Zhang R.Ranganath andK.Cho.2024. \u201cPreference learning algorithms do not learn preference rankings.\u201d InProceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS) 101928\u2013101968.","DOI":"10.52202\/079017-3234"},{"key":"e_1_2_8_16_1","unstructured":"Chen X. B.Fain L.Lyu andK.Munagala.2019. \u201cProportionally fair clustering.\u201d InProceedings of the 36th International Conference on Machine Learning (ICML) 1032\u20131041."},{"key":"e_1_2_8_17_1","unstructured":"Christiano P. F. J.Leike T.Brown M.Martic S.Legg andD.Amodei.2017. \u201cDeep reinforcement learning from human preferences.\u201d InProceedings of the 30th Annual Conference on Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_2_8_18_1","unstructured":"Conitzer V. R.Freedman J.Heitzig W. H.Holliday B. M.Jacobs N.Lambert M.Moss\u00e9 etal.2024. \u201cSocial choice should guide AI alignment in dealing with diverse human feedback.\u201d InProceedings of the 41st International Conference on Machine Learning (ICML)."},{"key":"e_1_2_8_19_1","unstructured":"Dai J. andE.Fleisig.2024. \u201cMapping social choice theory to RLHF.\u201darXiv preprint arXiv:2404.13038."},{"key":"e_1_2_8_20_1","unstructured":"Dragan A.2024. \u201cGoogle DeepMind: AIsafety\u22efok$\\mathrm{safety}\\dots \\mathrm{ok}$doomer: with Anca Dsragan.\u201dhttps:\/\/www.youtube.com\/watch?v=ZXA2dmFxXmg. YouTube video."},{"key":"e_1_2_8_21_1","volume-title":"Voting Procedures","author":"Dummett M","year":"1984"},{"key":"e_1_2_8_22_1","article-title":"A density estimation perspective on learning from pairwise human preferences","author":"Dumoulin V.","year":"2024","journal-title":"Transactions on Machine Learning Research"},{"key":"e_1_2_8_23_1","unstructured":"Ebadian S. andE.Micha.2025. \u201cBoosting sortition via proportional representation.\u201d InProceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Forthcoming."},{"key":"e_1_2_8_24_1","doi-asserted-by":"crossref","unstructured":"Fairstein R. G.Benad\u00e8 andK.Gal.2023. \u201cParticipatory budgeting designs for the real world.\u201d InProceedings of the AAAI Conference on Artificial Intelligence (AAAI) 5633\u20135640.","DOI":"10.1609\/aaai.v37i5.25699"},{"issue":"2017","key":"e_1_2_8_25_1","first-page":"27","article-title":"Multiwinner voting: A new challenge for social choice theory","volume":"74","author":"Faliszewski P.","year":"2017","journal-title":"Trends in Computational Social Choice"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2151-2"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2009.10.005"},{"key":"e_1_2_8_28_1","doi-asserted-by":"crossref","unstructured":"Feng S. T.Sorensen Y.Liu J.Fisher C. Y.Park Y.Choi andY.Tsvetkov.2024. \u201cModular pluralism: Pluralistic alignment via Multi\u2010LLM collaboration.\u201d InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) 4151\u20134171.","DOI":"10.18653\/v1\/2024.emnlp-main.240"},{"key":"e_1_2_8_29_1","unstructured":"Finn C. P.Abbeel andS.Levine.2017. \u201cModel\u2010agnostic meta\u2010learning for fast adaptation of deep networks.\u201d InInternational Conference on Machine Learning 1126\u20131135.PMLR."},{"key":"e_1_2_8_30_1","doi-asserted-by":"crossref","unstructured":"Freeman R. E.Micha andN.Shah.2021. \u201cTwo\u2010sided matching meets fair division.\u201d InProceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI\u201021) 203\u2013209.","DOI":"10.24963\/ijcai.2021\/29"},{"key":"e_1_2_8_31_1","doi-asserted-by":"crossref","unstructured":"Ge L. D.Halpern E.Micha A. D.Procaccia I.Shapira Y.Vorobeychik andJ.Wu.2024. \u201cAxioms for AI alignment from human feedback.\u201d InProceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS) 80439\u201380465.","DOI":"10.52202\/079017-2557"},{"key":"e_1_2_8_32_1","unstructured":"Ge L. B.Juba andY.Vorobeychik.2024. \u201cLearning linear utility functions from pairwise comparison queries.\u201darXiv preprint arXiv:2405.02612."},{"key":"e_1_2_8_33_1","unstructured":"Halpern D. E.Micha A. D.Procaccia andI.Shapira.2025. \u201cPairwise calibrated rewards for pluralistic alignment.\u201darXiv preprint arXiv:2506.06298."},{"key":"e_1_2_8_34_1","unstructured":"HejnaIII D. J. andD.Sadigh.2023. \u201cFew\u2010shot preference learning for human\u2010in\u2010the\u2010loop rl.\u201d InConference on Robot Learning 2014\u20132025.PMLR."},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01611-4"},{"key":"e_1_2_8_36_1","doi-asserted-by":"crossref","unstructured":"Kalayci Y. H. D.Kempe andV.Kher.2024. \u201cProportional representation in metric spaces and low\u2010distortion committee selection.\u201d InProceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI) 9815\u20139823.","DOI":"10.1609\/aaai.v38i9.28841"},{"key":"e_1_2_8_37_1","doi-asserted-by":"crossref","unstructured":"Kellerhals L. andJ.Peters.2024. \u201cProportional fairness in clustering: A social choice perspective.\u201d InProceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS).","DOI":"10.52202\/079017-3534"},{"key":"e_1_2_8_38_1","unstructured":"Khalifa M. H.Elsahar andM.Dymetman.2021. \u201cA distributional approach to controlled text generation.\u201d InProceedings of the 9th International Conference on Learning Representations (ICLR)."},{"key":"e_1_2_8_39_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-024-00820-y"},{"key":"e_1_2_8_40_1","unstructured":"Kirk R. I.Mediratta C.Nalmpantis J.Luketina E.Hambro E.Grefenstette andR.Raileanu.2024b. \u201cUnderstanding the effects of RLHF on LLM generalisation and diversity.\u201d InProceedings of the 12th International Conference on Learning Representations (ICLR)."},{"key":"e_1_2_8_41_1","doi-asserted-by":"crossref","unstructured":"Kupcsik A. D.Hsu andW. S.Lee.2018. \u201cLearning dynamic robot\u2010to\u2010human object handover from human feedback.\u201dRobotics Research: Volume 1 161\u2013176.","DOI":"10.1007\/978-3-319-51532-8_10"},{"key":"e_1_2_8_42_1","doi-asserted-by":"crossref","unstructured":"Lackner M. andP.Skowron.2022. \u201cApproval\u2010based committee voting.\u201d InMulti\u2010Winner Voting with Approval Preferences 1\u20137.Springer.","DOI":"10.1007\/978-3-031-09016-5_1"},{"key":"e_1_2_8_43_1","unstructured":"Lai F. X.Zhu H. V.Madhyastha andM.Chowdhury.2021. \u201cOort: Efficient federated learning via guided participant selection.\u201d In15th{USENIX}$\\lbrace{\\rm USENIX}\\rbrace$Symposium on Operating Systems Design and Implementation ({OSDI}$\\lbrace{\\rm OSDI}\\rbrace$21) 19\u201335."},{"key":"e_1_2_8_44_1","doi-asserted-by":"crossref","unstructured":"Lake T. E.Choi andG.Durrett.2025. \u201cFrom distributional to overton pluralism: Investigating large language model alignment.\u201d InProceedings of the 2025 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) 6794\u20136814.","DOI":"10.18653\/v1\/2025.naacl-long.346"},{"key":"e_1_2_8_45_1","unstructured":"Li B. L.Li A.Sun C.Wang andY.Wang.2021. \u201cApproximate group fairness for clustering.\u201d InProceedings of the 38th International Conference on Machine Learning (ICML) 6381\u20136391."},{"key":"e_1_2_8_46_1","unstructured":"Malik A. M.Wu V.Vasavada J.Song M.Coots J.Mitchell N.Goodman andC.Piech.2021. \u201cGenerative grading: Near human\u2010level accuracy for automated feedback on richly structured problems.\u201d InProceedings of the 14th International Conference on Educational Data Mining (EDM) 275\u2013286."},{"key":"e_1_2_8_47_1","unstructured":"Micha E. andN.Shah.2020. \u201cProportionally fair clustering revisited.\u201d InProceedings of the 47th International Colloquium on Automata Languages and Programming (ICALP) 85:1\u201385:16."},{"key":"e_1_2_8_48_1","doi-asserted-by":"crossref","unstructured":"Mishra A. 2023. \u201cAI alignment and social choice: Fundamental limitations and policy implications.\u201darXiv preprint arXiv:2310.16048.","DOI":"10.2139\/ssrn.4605509"},{"key":"e_1_2_8_49_1","unstructured":"Ouyang L. J.Wu X.Jiang D.Almeida C.Wainwright P.Mishkin C.Zhang S.Agarwal K.Slama A.Ray etal.2022. \u201cTraining language models to follow instructions with human feedback.\u201d InProceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS) 27730\u201327744."},{"key":"e_1_2_8_50_1","unstructured":"Park C. M.Liu D.Kong K.Zhang andA.Ozdaglar.2024. \u201cRLHF from heterogeneous feedback via personalization and preference aggregation.\u201darXiv preprint arXiv:2405.00254."},{"key":"e_1_2_8_51_1","doi-asserted-by":"crossref","unstructured":"Perez E. S.Huang F.Song T.Cai R.Ring J.Aslanides A.Glaese N.McAleese andG.Irving.2022. \u201cRed teaming language models with language models.\u201d InProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP) 3419\u20133448.","DOI":"10.18653\/v1\/2022.emnlp-main.225"},{"key":"e_1_2_8_52_1","doi-asserted-by":"crossref","unstructured":"Perez E. S.Ringer K.Lukosiute K.Nguyen E.Chen S.Heiner C.Pettit etal.2023. \u201cDiscovering language model behaviors with model\u2010written evaluations.\u201d InFindings of the Association for Computational Linguistics: ACL 2023 13387\u201313434.","DOI":"10.18653\/v1\/2023.findings-acl.847"},{"key":"e_1_2_8_53_1","doi-asserted-by":"crossref","unstructured":"Peters D.2024.Proportional representation for artificial intelligence.","DOI":"10.3233\/FAIA240463"},{"key":"e_1_2_8_54_1","unstructured":"Peters D. G.Pierczy\u0144ski andP.Skowron.2021. \u201cProportional participatory budgeting with additive utilities.\u201d InProceedings of the 34th Annual Conference on Neural Information Processing Systems (NeurIPS) 12726\u201312737."},{"key":"e_1_2_8_55_1","doi-asserted-by":"crossref","unstructured":"Peters D. andP.Skowron.2020. \u201cProportionality and the limits of welfarism.\u201d InProceedings of the 21st ACM Conference on Economics and Computation 793\u2013794.","DOI":"10.1145\/3391403.3399465"},{"key":"e_1_2_8_56_1","unstructured":"Poddar S. Y.Wan H.Ivison A.Gupta andN.Jaques.2024. \u201cPersonalizing reinforcement learning from human feedback with variational preference learning.\u201d InProceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_2_8_57_1","unstructured":"Procaccia A. B.Schiffer S.Wang andS.Zhang.2025. \u201cMetritocracy: representative metrics for lite benchmarks.\u201darXiv preprint arXiv:2506.09813."},{"key":"e_1_2_8_58_1","unstructured":"Revel M. S.Milli T.Lu J.Watson\u2010Daniels andM.Nickel.2025. \u201cRepresentative ranking for deliberation in the public sphere.\u201d InProceedings of the 42nd International Conference on Machine Learning (ICML)."},{"key":"e_1_2_8_59_1","unstructured":"Sarwar B. M. G.Karypis J.Konstan andJ.Riedl.2002. \u201cRecommender systems for large\u2010scale e\u2010commerce: Scalable neighborhood formation using clustering.\u201d InProceedings of the fifth international conference on computer and information technology volume 1 291\u2013324."},{"key":"e_1_2_8_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"e_1_2_8_61_1","doi-asserted-by":"crossref","unstructured":"Shah N.2023. \u201cPushing the limits of fairness in algorithmic decision\u2010making.\u201d InIJCAI 7051\u20137056.","DOI":"10.24963\/ijcai.2023\/806"},{"key":"e_1_2_8_62_1","doi-asserted-by":"crossref","unstructured":"Shi Y. Z.Liu Z.Shi andH.Yu.2023. \u201cFairness\u2010aware client selection for federated learning.\u201d In2023 IEEE international conference on multimedia and expo (ICME) 324\u2013329.IEEE.","DOI":"10.1109\/ICME55011.2023.00063"},{"key":"e_1_2_8_63_1","unstructured":"Shypula A. S.Li B.Zhang V.Padmakumar K.Yin andO.Bastani.2025. \u201cEvaluating the diversity and quality of LLM generated content.\u201darXiv preprint arXiv:2504.12522."},{"key":"e_1_2_8_64_1","unstructured":"Si Z. S.Hu K.Ji andS.Lyu.2024. \u201cMeta\u2010learning with heterogeneous Tasks.\u201darXiv preprint arXiv:2410.18894."},{"key":"e_1_2_8_65_1","unstructured":"Siththaranjan A. C.Laidlaw andHadfield\u2010Menell D.2024. \u201cDistributional preference learning: Understanding and accounting for hidden context in RLHF.\u201d InProceedings of the 12th International Conference on Learning Representations (ICLR)."},{"issue":"2","key":"e_1_2_8_66_1","first-page":"1","article-title":"Polis: scaling deliberation by mapping high dimensional opinion spaces","volume":"26","author":"Small C.","year":"2021","journal-title":"Recerca: revista de pensament i an\u00e0lisi"},{"key":"e_1_2_8_67_1","unstructured":"Sorensen T. J.Moore et\u00a0al.2024. \u201cA roadmap to pluralistic alignment.\u201d InProceedings of the 41st International Conference on Machine Learning (ICML). Position paper."},{"key":"e_1_2_8_68_1","unstructured":"Stiennon N. L.Ouyang J.Wu D. M.Ziegler R.Lowe C.Voss A.Radford D.Amodei andP.Christiano.2020. \u201cLearning to summarize from human feedback.\u201d InProceeding of the 31st Annual Conference on Neural Information Processing Systems (NeurIPS) 3008\u20133021."},{"key":"e_1_2_8_69_1","unstructured":"Swamy G. C.Dann R.Kidambi Z. S.Wu andA.Agarwal.2024. \u201cA Minimaximalist approach to reinforcement learning from human feedback.\u201d InProceedings of the 41st International Conference on Machine Learning (ICML)."},{"key":"e_1_2_8_70_1","unstructured":"Viappiani P. andC.Boutilier.2010. \u201cOptimal Bayesian recommendation sets and myopically optimal choice query sets.\u201d InProceedings of the 6th Annual Conference on Neural Information Processing Systems (NeurIPS) 2352\u20132360."},{"key":"e_1_2_8_71_1","doi-asserted-by":"crossref","unstructured":"Wang H. Z.Kaplan D.Niu andB.Li.2020. \u201cOptimizing federated learning on non\u2010iid data with reinforcement learning.\u201d InIEEE INFOCOM 2020\u2010IEEE Conference on Computer Communications 1698\u20131707.IEEE.","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"e_1_2_8_72_1","unstructured":"Weidinger L. J.Mellor M.Rauh C.Griffin J.Uesato P.\u2010S.Huang M.Cheng M.Glaese B.Balle A.Kasirzadeh et\u00a0al.2021. \u201cEthical and social risks of harm from language models.\u201darXiv:2112.04359."},{"key":"e_1_2_8_73_1","unstructured":"Yu T. D.Quillen Z.He R.Julian K.Hausman C.Finn andS.Levine.2020. \u201cMeta\u2010world: A benchmark and evaluation for multi\u2010task and meta reinforcement learning.\u201d InConference on robot learning 1094\u20131100.PMLR."},{"key":"e_1_2_8_74_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-015-0756-9"},{"key":"e_1_2_8_75_1","unstructured":"Zhong H. Z.Deng W. J.Su Z. S.Wu andL.Zhang.2024. \u201cProvable multi\u2010party reinforcement learning with diverse human feedback.\u201darXiv preprint arXiv:2403.05006."},{"key":"e_1_2_8_76_1","unstructured":"Zhu B. M.Jordan andJ.Jiao.2023. \u201cPrincipled reinforcement learning with human feedback from pairwise or k\u2010wise comparisons.\u201d InProceedings of the 40th International Conference on Machine Learning (ICML) 43037\u201343067."},{"key":"e_1_2_8_77_1","unstructured":"Ziegler D. M. N.Stiennon J.Wu T. B.Brown A.Radford D.Amodei P.Christiano andG.Irving.2019. \u201cFine\u2010tuning language models from human preferences.\u201darXiv preprint arXiv:1909.08593."}],"container-title":["AI Magazine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/aaai.70044","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T06:05:53Z","timestamp":1768025153000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/aaai.70044"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":76,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["10.1002\/aaai.70044"],"URL":"https:\/\/doi.org\/10.1002\/aaai.70044","archive":["Portico"],"relation":{},"ISSN":["0738-4602","2371-9621"],"issn-type":[{"value":"0738-4602","type":"print"},{"value":"2371-9621","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"2025-08-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70044"}}