[{"publication_identifier":{"eissn":["2184-433X"],"issn":["2184-3589"],"isbn":["9789897587962"]},"OA_type":"green","researchdata_availability":"no","citation":{"apa":"Cano Cordoba, F. (2026). Explaining decisions one conversation at a time: Opportunities and risks of LLMs as explainability assistants. In <i>Proceedings of the 18th International Conference on Agents and Artificial Intelligence</i> (Vol. 5, pp. 4689–4696). Marbella, Spain: Science and Technology Publications. <a href=\"https://doi.org/10.5220/0014483200004052\">https://doi.org/10.5220/0014483200004052</a>","short":"F. Cano Cordoba, in:, Proceedings of the 18th International Conference on Agents and Artificial Intelligence, Science and Technology Publications, 2026, pp. 4689–4696.","ieee":"F. Cano Cordoba, “Explaining decisions one conversation at a time: Opportunities and risks of LLMs as explainability assistants,” in <i>Proceedings of the 18th International Conference on Agents and Artificial Intelligence</i>, Marbella, Spain, 2026, vol. 5, pp. 4689–4696.","chicago":"Cano Cordoba, Filip. “Explaining Decisions One Conversation at a Time: Opportunities and Risks of LLMs as Explainability Assistants.” In <i>Proceedings of the 18th International Conference on Agents and Artificial Intelligence</i>, 5:4689–96. Science and Technology Publications, 2026. <a href=\"https://doi.org/10.5220/0014483200004052\">https://doi.org/10.5220/0014483200004052</a>.","mla":"Cano Cordoba, Filip. “Explaining Decisions One Conversation at a Time: Opportunities and Risks of LLMs as Explainability Assistants.” <i>Proceedings of the 18th International Conference on Agents and Artificial Intelligence</i>, vol. 5, Science and Technology Publications, 2026, pp. 4689–96, doi:<a href=\"https://doi.org/10.5220/0014483200004052\">10.5220/0014483200004052</a>.","ama":"Cano Cordoba F. Explaining decisions one conversation at a time: Opportunities and risks of LLMs as explainability assistants. In: <i>Proceedings of the 18th International Conference on Agents and Artificial Intelligence</i>. Vol 5. Science and Technology Publications; 2026:4689-4696. doi:<a href=\"https://doi.org/10.5220/0014483200004052\">10.5220/0014483200004052</a>","ista":"Cano Cordoba F. 2026. Explaining decisions one conversation at a time: Opportunities and risks of LLMs as explainability assistants. Proceedings of the 18th International Conference on Agents and Artificial Intelligence. ICAART: International Conference on Agents and Artificial Intelligence vol. 5, 4689–4696."},"article_processing_charge":"No","das_tickbox":"0","OA_place":"repository","oa_version":"Accepted Version","day":"01","oa":1,"keyword":["Explainable AI","Large Language Models","Trust in AI"],"date_created":"2026-06-21T22:03:00Z","main_file_link":[{"url":"https://filipcano.org/files/icaart26llm.pdf","open_access":"1"}],"date_updated":"2026-06-24T08:37:00Z","department":[{"_id":"ToHe"}],"year":"2026","_id":"22103","quality_controlled":"1","acknowledgement":"This work has been supported by the European Research Council under Grant No.: ERC-2020-AdG\r\n101020093. LLM–based tools have been used as\r\nwriting assistance to help improve presentation.\r\n","abstract":[{"lang":"eng","text":"Modern AI systems increasingly rely on opaque, highly complex models whose inner workings remain inaccessible even to experts. This opacity creates challenges for trust, accountability, and compliance with\r\nemerging regulatory expectations such as the “right to an explanation”. While traditional explainability methods—feature attributions, counterfactuals, surrogate models—and interpretable model classes provide valuable insights for engineers, they often fall short of delivering the contextual, conversational explanations that\r\nreal users expect. Large Language Models (LLMs) offer a promising new avenue for explanation due to their\r\nability to engage interactively, adapt to user needs, and translate technical outputs into more accessible reasoning. However, their tendencies toward hallucination, conflict avoidance, and oversimplification introduce\r\nserious risks when used as explanatory agents. This paper analyzes these opportunities and limitations, examines verification strategies for ensuring explanation fidelity, and situates LLM-generated explanations within\r\nbroader concerns about public trust. The paper concludes by outlining best practices and future research directions for building robust, verifiable, and human-aligned explanation systems."}],"corr_author":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","project":[{"call_identifier":"H2020","grant_number":"101020093","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","name":"Vigilant Algorithmic Monitoring of Software"}],"scopus_import":"1","month":"04","type":"conference","conference":{"name":"ICAART: International Conference on Agents and Artificial Intelligence","location":"Marbella, Spain","start_date":"2026-03-05","end_date":"2026-03-08"},"supplementarymaterial":"no","doi":"10.5220/0014483200004052","publication":"Proceedings of the 18th International Conference on Agents and Artificial Intelligence","page":"4689-4696","volume":5,"publication_status":"published","status":"public","intvolume":"         5","title":"Explaining decisions one conversation at a time: Opportunities and risks of LLMs as explainability assistants","publisher":"Science and Technology Publications","date_published":"2026-04-01T00:00:00Z","ec_funded":1,"author":[{"full_name":"Cano Cordoba, Filip","first_name":"Filip","last_name":"Cano Cordoba","orcid":"0000-0002-0783-904X","id":"708cad98-e86a-11ef-8098-bdae2d7c6af1"}],"language":[{"iso":"eng"}]}]