GenAI-content explainable detection system

Lewis Moore; Joanna Olszewska

doi:10.5220/0014974400004018

GenAI-content explainable detection system

Lewis Moore
, Joanna Olszewska

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

25 Downloads (Pure)

Abstract

With the rise of Generative Artificial Intelligence (GenAI) and especially Large Language Models (LLMs) to produce essays, courseworks, reviews, papers, books, news, etc., it is important to be able to differentiate between such AI-generated content and human-generated one. Hence, our paper proposes to detect text which has been produced by GenAI-based chatbots. Our detection system uses an explainable Artificial Intelligence (XAI) approach based on a Multinomial Naive Bayes (MNB) model, and it offers a free, open-source, and dataprivacy compliant tool for enterprises. Furthermore, it shows promising performances in terms of accuracy and sustainability when compared with existing freewares.

Original language	English
Title of host publication	Proceedings of the 28th International Conference on Enterprise Information Systems
Subtitle of host publication	May 22-24, 2026, in Benidorm, Spain
Editors	Joaquim Filipe, Michał Śmiałek, Alexander Brodsky, Slimane Hammoudi
Place of Publication	Setúbal, Portugal
Publisher	SciTePress
Pages	1205-1216
Number of pages	12
Volume	2
ISBN (Print)	9789897588341
DOIs	https://doi.org/10.5220/0014974400004018
Publication status	Published - 22 May 2026
Event	28th International Conference on Enterprise Information Systems - Benidorm, Spain Duration: 22 May 2026 → 24 May 2026 https://iceis.scitevents.org/?y=2026

Publication series

Name	Conference Proceedings
Publisher	SciTePress
ISSN (Electronic)	2184-4992

Conference

Conference	28th International Conference on Enterprise Information Systems
Abbreviated title	ICEIS 2026
Country/Territory	Spain
City	Benidorm
Period	22/05/26 → 24/05/26
Internet address	https://iceis.scitevents.org/?y=2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education
SDG 9 Industry, Innovation, and Infrastructure
SDG 16 Peace, Justice and Strong Institutions

Keywords

intelligent agents
autonomous agents
generative artificial intelligence (GenAI)
large language models (LLMs)
deep learning
trust and transparency
accountability and responsibility
explainable artificial intelligence (XAI)
human-computer interfaces supporting XAI
decision support systems
applications of expert systms

Access to Document

10.5220/0014974400004018

2026 03 25 Moore et al GenAI acceptedAccepted author manuscript, 2.74 MB

Cite this

Moore, Lewis ; Olszewska, Joanna. / GenAI-content explainable detection system. Proceedings of the 28th International Conference on Enterprise Information Systems: May 22-24, 2026, in Benidorm, Spain. editor / Joaquim Filipe ; Michał Śmiałek ; Alexander Brodsky ; Slimane Hammoudi. Vol. 2 Setúbal, Portugal : SciTePress, 2026. pp. 1205-1216 (Conference Proceedings).

@inproceedings{c70e3468f63d4d359d869ad629c66de6,

title = "GenAI-content explainable detection system",

abstract = "With the rise of Generative Artificial Intelligence (GenAI) and especially Large Language Models (LLMs) to produce essays, courseworks, reviews, papers, books, news, etc., it is important to be able to differentiate between such AI-generated content and human-generated one. Hence, our paper proposes to detect text which has been produced by GenAI-based chatbots. Our detection system uses an explainable Artificial Intelligence (XAI) approach based on a Multinomial Naive Bayes (MNB) model, and it offers a free, open-source, and dataprivacy compliant tool for enterprises. Furthermore, it shows promising performances in terms of accuracy and sustainability when compared with existing freewares.",

keywords = "intelligent agents, autonomous agents, generative artificial intelligence (GenAI), large language models (LLMs), deep learning, trust and transparency, accountability and responsibility, explainable artificial intelligence (XAI), human-computer interfaces supporting XAI, decision support systems, applications of expert systms",

author = "Lewis Moore and Joanna Olszewska",

year = "2026",

month = may,

day = "22",

doi = "10.5220/0014974400004018",

language = "English",

isbn = "9789897588341",

volume = "2",

series = "Conference Proceedings",

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pages = "1205--1216",

editor = "Joaquim Filipe and Micha{\l} {\'S}mia{\l}ek and Alexander Brodsky and Slimane Hammoudi",

booktitle = "Proceedings of the 28th International Conference on Enterprise Information Systems",

address = "Portugal",

note = "28th International Conference on Enterprise Information Systems, ICEIS 2026 ; Conference date: 22-05-2026 Through 24-05-2026",

url = "https://iceis.scitevents.org/?y=2026",

}

Moore, L & Olszewska, J 2026, GenAI-content explainable detection system. in J Filipe, M Śmiałek, A Brodsky & S Hammoudi (eds), Proceedings of the 28th International Conference on Enterprise Information Systems: May 22-24, 2026, in Benidorm, Spain. vol. 2, Conference Proceedings, SciTePress, Setúbal, Portugal, pp. 1205-1216, 28th International Conference on Enterprise Information Systems, Benidorm, Spain, 22/05/26. https://doi.org/10.5220/0014974400004018

GenAI-content explainable detection system. / Moore, Lewis; Olszewska, Joanna.
Proceedings of the 28th International Conference on Enterprise Information Systems: May 22-24, 2026, in Benidorm, Spain. ed. / Joaquim Filipe; Michał Śmiałek; Alexander Brodsky; Slimane Hammoudi. Vol. 2 Setúbal, Portugal: SciTePress, 2026. p. 1205-1216 (Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - GenAI-content explainable detection system

AU - Moore, Lewis

AU - Olszewska, Joanna

PY - 2026/5/22

Y1 - 2026/5/22

N2 - With the rise of Generative Artificial Intelligence (GenAI) and especially Large Language Models (LLMs) to produce essays, courseworks, reviews, papers, books, news, etc., it is important to be able to differentiate between such AI-generated content and human-generated one. Hence, our paper proposes to detect text which has been produced by GenAI-based chatbots. Our detection system uses an explainable Artificial Intelligence (XAI) approach based on a Multinomial Naive Bayes (MNB) model, and it offers a free, open-source, and dataprivacy compliant tool for enterprises. Furthermore, it shows promising performances in terms of accuracy and sustainability when compared with existing freewares.

AB - With the rise of Generative Artificial Intelligence (GenAI) and especially Large Language Models (LLMs) to produce essays, courseworks, reviews, papers, books, news, etc., it is important to be able to differentiate between such AI-generated content and human-generated one. Hence, our paper proposes to detect text which has been produced by GenAI-based chatbots. Our detection system uses an explainable Artificial Intelligence (XAI) approach based on a Multinomial Naive Bayes (MNB) model, and it offers a free, open-source, and dataprivacy compliant tool for enterprises. Furthermore, it shows promising performances in terms of accuracy and sustainability when compared with existing freewares.

KW - intelligent agents

KW - autonomous agents

KW - generative artificial intelligence (GenAI)

KW - large language models (LLMs)

KW - deep learning

KW - trust and transparency

KW - accountability and responsibility

KW - explainable artificial intelligence (XAI)

KW - human-computer interfaces supporting XAI

KW - decision support systems

KW - applications of expert systms

U2 - 10.5220/0014974400004018

DO - 10.5220/0014974400004018

M3 - Conference contribution

SN - 9789897588341

VL - 2

T3 - Conference Proceedings

SP - 1205

EP - 1216

BT - Proceedings of the 28th International Conference on Enterprise Information Systems

A2 - Filipe, Joaquim

A2 - Śmiałek, Michał

A2 - Brodsky, Alexander

A2 - Hammoudi, Slimane

PB - SciTePress

CY - Setúbal, Portugal

T2 - 28th International Conference on Enterprise Information Systems

Y2 - 22 May 2026 through 24 May 2026

ER -

GenAI-content explainable detection system

Abstract

Publication series

Conference

UN SDGs

Keywords

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