{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:04:05Z","timestamp":1784203445844,"version":"3.55.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:p>Contestable AI requires that AI-driven decisions align with given preferences. Various types of argumentation frameworks have been shown to support forms of contestability. In this paper we focus on the little-studied Edge-Weighted Quantitative Bipolar Argumentation Frameworks (EW-QBAFs), where arguments have a base score as in QBAFs but attacks and supports (edges) are weighted. After generalising gradual semantics and properties thereof from QBAFs to EW-QBAFs, we introduce the contestability problem for EW-QBAFs, which asks how to modify edge weights to achieve a desired strength for a specific topic argument. To address this problem, we propose  gradient-based relation attribution explanations (G-RAEs), which quantify the sensitivity of the topic argument's strength to changes in individual edge weights, thus providing interpretable guidance for weight adjustments towards contestability. Building on G-RAEs, we develop a heuristic algorithm that progressively adjusts the edge weights to attain the desired strength. We evaluate our approach experimentally on synthetic EW-QBAFs that simulate the structural characteristics of personalised recommender systems and multi-layer perceptrons, demonstrating that it can support contestability effectively.<\/jats:p>","DOI":"10.24963\/kr.2026\/64","type":"proceedings-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:15:53Z","timestamp":1784200553000},"page":"676-687","source":"Crossref","is-referenced-by-count":0,"title":["Contestability in Edge-Weighted Quantitative Bipolar Argumentation Frameworks"],"prefix":"10.24963","author":[{"given":"Xiang","family":"Yin","sequence":"first","affiliation":[{"name":"Imperial College London"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nico","family":"Potyka","sequence":"additional","affiliation":[{"name":"Cardiff University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Rago","sequence":"additional","affiliation":[{"name":"King's College London"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timotheus","family":"Kampik","sequence":"additional","affiliation":[{"name":"Ume\u00e5 University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Toni","sequence":"additional","affiliation":[{"name":"Imperial College London"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"23nd International Conference on Principles of Knowledge Representation and Reasoning {KR-2026}","theme":"Artificial Intelligence","location":"Lisbon, Portuagal","acronym":"KR-2026","number":"23","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","European Association for Artificial Intelligence"],"start":{"date-parts":[[2026,7,20]]},"end":{"date-parts":[[2026,7,18]]}},"container-title":["Proceedings of the TwentyThird International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:16:11Z","timestamp":1784200571000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2026\/64"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2026\/64","relation":{},"subject":[],"published":{"date-parts":[[2026,7]]}}}