---
res:
  bibo_abstract:
  - "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.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Filip
      foaf_name: Cano Cordoba, Filip
      foaf_surname: Cano Cordoba
      foaf_workInfoHomepage: http://www.librecat.org/personId=708cad98-e86a-11ef-8098-bdae2d7c6af1
    orcid: 0000-0002-0783-904X
  bibo_doi: 10.5220/0014483200004052
  bibo_volume: 5
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2184-3589
  - http://id.crossref.org/issn/2184-433X
  - http://id.crossref.org/issn/9789897587962
  dct_language: eng
  dct_publisher: Science and Technology Publications@
  dct_subject:
  - Explainable AI
  - Large Language Models
  - Trust in AI
  dct_title: 'Explaining decisions one conversation at a time: Opportunities and risks
    of LLMs as explainability assistants@'
...