948 research outputs found
Combating Misinformation in the Age of LLMs: Opportunities and Challenges
Misinformation such as fake news and rumors is a serious threat on
information ecosystems and public trust. The emergence of Large Language Models
(LLMs) has great potential to reshape the landscape of combating
misinformation. Generally, LLMs can be a double-edged sword in the fight. On
the one hand, LLMs bring promising opportunities for combating misinformation
due to their profound world knowledge and strong reasoning abilities. Thus, one
emergent question is: how to utilize LLMs to combat misinformation? On the
other hand, the critical challenge is that LLMs can be easily leveraged to
generate deceptive misinformation at scale. Then, another important question
is: how to combat LLM-generated misinformation? In this paper, we first
systematically review the history of combating misinformation before the advent
of LLMs. Then we illustrate the current efforts and present an outlook for
these two fundamental questions respectively. The goal of this survey paper is
to facilitate the progress of utilizing LLMs for fighting misinformation and
call for interdisciplinary efforts from different stakeholders for combating
LLM-generated misinformation.Comment: 9 pages for the main paper, 35 pages including 656 references, more
resources on "LLMs Meet Misinformation" are on the website:
https://llm-misinformation.github.io
Design requirements for generating deceptive content to protect document repositories
For nearly 30 years, fake digital documents have been used to identify external intruders and malicious insider threats. Unfortunately, while fake files hold potential to assist in data theft detection, there is little evidence of their application outside of niche organisations and academic institutions. The barrier to wider adoption appears to be the difficulty in constructing deceptive content. The current generation of solutions principally: (1) use unrealistic random data; (2) output heavily formatted or specialised content, that is difficult to apply to other environments; (3) require users to manually build the content, which is not scalable, or (4) employ an existing production file, which creates a protection paradox. This paper introduces a set of requirements for generating automated fake file content: (1) enticing, (2) realistic, (3) minimise disruption, (4) adaptive, (5) scalable protective coverage, (6) minimise sensitive artefacts and copyright infringement, and (7) contain no distinguishable characteristics. These requirements have been drawn from literature on natural science, magical performances, human deceit, military operations, intrusion detection and previous fake file solutions. These requirements guide the design of an automated fake file content construction system, providing an opportunity for the next generation of solutions to find greater commercial application and widespread adoption
Machine Learning-based Lie Detector applied to a Novel Annotated Game Dataset
Lie detection is considered a concern for everyone in their day to day life
given its impact on human interactions. Thus, people normally pay attention to
both what their interlocutors are saying and also to their visual appearances,
including faces, to try to find any signs that indicate whether the person is
telling the truth or not. While automatic lie detection may help us to
understand this lying characteristics, current systems are still fairly
limited, partly due to lack of adequate datasets to evaluate their performance
in realistic scenarios. In this work, we have collected an annotated dataset of
facial images, comprising both 2D and 3D information of several participants
during a card game that encourages players to lie. Using our collected dataset,
We evaluated several types of machine learning-based lie detectors in terms of
their generalization, person-specific and cross-domain experiments. Our results
show that models based on deep learning achieve the best accuracy, reaching up
to 57\% for the generalization task and 63\% when dealing with a single
participant. Finally, we also highlight the limitation of the deep learning
based lie detector when dealing with cross-domain lie detection tasks
Online Handbook of Argumentation for AI: Volume 1
This volume contains revised versions of the papers selected for the first
volume of the Online Handbook of Argumentation for AI (OHAAI). Previously,
formal theories of argument and argument interaction have been proposed and
studied, and this has led to the more recent study of computational models of
argument. Argumentation, as a field within artificial intelligence (AI), is
highly relevant for researchers interested in symbolic representations of
knowledge and defeasible reasoning. The purpose of this handbook is to provide
an open access and curated anthology for the argumentation research community.
OHAAI is designed to serve as a research hub to keep track of the latest and
upcoming PhD-driven research on the theory and application of argumentation in
all areas related to AI.Comment: editor: Federico Castagna and Francesca Mosca and Jack Mumford and
Stefan Sarkadi and Andreas Xydi
An Evaluation Methodology of Named Entities Recognition in Spanish Language: ECU 911 Case Study
The importance of the gathered information in Integrated Security Services as ECU911 in Ecuador is evidenced in terms of its quality and availability in order to perform decision-making tasks. It is a priority to avoid the loss of relevant information such as event address, places references, names, etc. In this context it is present Named Entity Recognition (NER) analysis for discovering information into informal texts. Unlike structured corpus and labeled for NER analysis like CONLL2002 or ANCORA, informal texts generated from emergency call dialogues have a very wide linguistic variety; in addition, there is a strong tending to lose important information in their processing. A relevant aspect to considerate is the identification of texts that denotes entities such as the physical address where emergency events occurred. This study aims to extract the locations in which an emergency event has been issued. A set of experiments was performed with NER models based on Convolutional Neural Network (CNN). The performance of models was evaluated according to parameters such as training dataset size, dropout rate, location dictionary, and denoting location. An experimentation methodology was proposed, with it follows the next steps: i) Data preprocessing, ii) Dataset labeling, iii) Model structuring, and iv) Model evaluating. Results revealed that the performance of a model improves when having more training data, an adequate dropout rate to control overfitting problems, and a combination of a dictionary of locations and replacing words denoting entities
Smart Collaboration in Global Virtual Teams: The Influence of Culture on Technology Acceptance and Communication Effectiveness
Teams are beginning to rely on smart communication technology that is enhanced by Artificial Intelligence (AI). Yet, we lack understanding of how these smart communication technologies (SCT) influence team collaboration, especially in global virtual teams (GVT). This study empirically investigates how cultural values and practices influence the acceptance of SCT and how the use of this technology impacts communication effectiveness in GVT. We surveyed 643 members of 109 GVT before and after using the SCT. Results showed that team members from individualistic, future oriented cultures generally had more positive expectations towards the performance and enjoyment of using the technology. Uncertainty avoidance increased effort expectancy. After using SCT for communicating in the GVT, most differences disappeared. Regarding communication effectiveness, SCT had a positive influence, which was stronger for performance and future oriented cultures
A Discussion Game for the Credulous Decision Problem of Abstract Dialectical Frameworks under Preferred Semantics
Abstract dialectical frameworks (ADFs) have been introduced as a general formalism for modeling and evaluating argumentation. However, the role of discussion in reasoning in ADFs has not been clarified well so far. The current work presents a discussion game, as a proof method, to answer credulous decision problems of ADFs under preferred semantics. The game can be the basis for an algorithm that can be used not only for answering the decision problem but also for human-machine interaction
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