{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:07:05Z","timestamp":1764850025658,"version":"3.46.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686387","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>This paper presents the Concil-IA project, an initiative exploring how machine learning (ML) can promote self-composition in conflict resolution. The research investigates whether an ML model can predict judicial outcomes and assist parties in reaching agreements during end-procedural conciliation hearings. We collected judgments (textual documents) from the Brazilian Special Civil Court at the Federal University of Santa Catarina (JEC-UFSC), focusing on disputes in air transport services (Consumer Law) where parties require compensation value for immaterial damage. We developed an ML model following a structured pipeline, including preprocessing, data representation and labeling, model training and testing using regression algorithms, and practical application in the legal domain. The model was validated in two phases: (i) presenting predicted judgment values to parties during conciliation hearings for new, similar cases; and (ii) comparing actual judgment values with the model\u2019s predictions after the proceedings concluded. The model showed satisfactory performance with minimal deviations. Predicted values were well received by parties and their legal representatives and closely aligned with actual judgments. We conclude that an ML model can effectively support self-composition in conflict resolution, reduce procedural uncertainties, and increase the likelihood of consensual settlements in judicial disputes.<\/jats:p>","DOI":"10.3233\/faia251586","type":"book-chapter","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:04:52Z","timestamp":1764849892000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning to Promote Self-Composition in Conflict Resolution: The Brazilian Concil-IA Project"],"prefix":"10.3233","author":[{"given":"Isabela","family":"Sabo","sequence":"first","affiliation":[{"name":"Department of Law, Federal University of Santa Catarina, Florian\u00f3polis, Brazil"}]},{"given":"Thiago","family":"Dal Pont","sequence":"additional","affiliation":[{"name":"Automation and Systems Department, Federal University of Santa Catarina, Florian\u00f3polis, Brazil"}]},{"given":"Aires","family":"Rover","sequence":"additional","affiliation":[{"name":"Department of Law, Federal University of Santa Catarina, Florian\u00f3polis, Brazil"}]},{"given":"Jomi","family":"Hubner","sequence":"additional","affiliation":[{"name":"Automation and Systems Department, Federal University of Santa Catarina, Florian\u00f3polis, Brazil"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Legal Knowledge and Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251586","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:04:53Z","timestamp":1764849893000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251586"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"ISBN":["9781643686387"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251586","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,2]]}}}