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Conversational versus Dashboard Explainable AI for UAV Intrusion Detection: An Empirical Study of Operator Trust and Reliance — arXiv:2608.10434

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.10434
License (as archived): CC0-1.0
Archived: 2026-10-19T06:08:22.413Z
SHA-256 of the archived text: 1f5c44496cb6d901904bb3777bb4562df90d2c5e9d0a4a1aa84c99000e1d88c1
Stable URL of this page: https://metraipolis.com/documento/dc_5bbadabb71bb

How to cite

Conversational versus Dashboard Explainable AI for UAV Intrusion Detection: An Empirical Study of Operator Trust and Reliance — arXiv:2608.10434 — arXiv (en), https://arxiv.org/abs/2608.10434 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5bbadabb71bb (Metraipolis archive dc_5bbadabb71bb, sha256 1f5c44496cb6d901904bb3777bb4562df90d2c5e9d0a4a1aa84c99000e1d88c1)

BibTeX

@misc{dc5bbadabb71bb,
  title = {Conversational versus Dashboard Explainable AI for UAV Intrusion Detection: An Empirical Study of Operator Trust and Reliance — arXiv:2608.10434},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.10434},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5bbadabb71bb; sha256 1f5c44496cb6d901904bb3777bb4562df90d2c5e9d0a4a1aa84c99000e1d88c1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.10434. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Machine learning-based Intrusion Detection Systems (IDS) have demonstrated superior performance in securing Unmanned Aerial Vehicle (UAV) networks. However, the 'black-box' nature of these models, combined with the high dimensionality of multimodal cyber-physical data, poses significant interpretability challenges. Static visualization dashboards may struggle to present complex relationships among multimodal cyber-physical features in a form that is easy for operators to inspect and interpret. To address this, we propose a Conversational XAI interface powered by Large Language Models (LLM) to facilitate on-demand investigation. In a controlled experiment with participants, we systematically evaluated the impact of this conversational interface versus a traditional XAI Dashboard on operator understanding, trust, and reliance during post-incident auditing tasks. Our results suggest that the conversational interface was perceived as more useful than the dashboard, potentially because it helped participants access and synthesize relevant information more easily. However, this benefit was accompanied by a lower level of appropriate self-reliance, indicating a potential risk of over-reliance. One possible interpretation is that the natural-language responses made the AI advice easier to accept, which may have reduced participants' tendency to verify the underlying evidence when the IDS was incorrect. These findings point to a potential trade-off in human-AI collaboration for UAV intrusion auditing: interaction mechanisms that improve perceived usability may also increase the risk of inappropriate reliance. We conclude by discussing design implications for future XAI systems that balance seamless interaction with cognitive forcing functions to foster appropriate reliance.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-12 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.10434 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:57:28.266Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-12?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).