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Future Internet
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Information Networks with Human-Centric AI
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Information Networks with Human-Centric AI

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 21564

Special Issue Editors

Dr. Massimo Stella

E-Mail Website
Guest Editor
Department of Psychology and Cognitive Science, University of Trento, Corso Bettini 33, 38068 Rovereto, Italy
Interests: cognitive data science; complex networks; knowledge modelling; multiplex networks; natural language processing
Special Issues, Collections and Topics in MDPI journals
Dr. Giulio Rossetti

E-Mail Website
Guest Editor
Knowledge Discovery and Data mining Laboratory, Information Science and Technologies Institute, Italian National Research Council, 56124 Pisa, Italy
Interests: dynamic networks; community detection; diffusion processes; feature-rich networks; human mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The investigation of information networks is a rapidly growing research area, where phenomena relative to knowledge building and flow can be analyzed with powerful frameworks like complex networks and artificial intelligence (AI). Researchers contributing to this innovative research area come from a variety of fields, including computer science, business, mathematics, linguistics, physics, social science, and psychology.

Investigations of networks of flowing information or knowledge often feature opaque, black-box models of artificial intelligence, where machine learning provides access only to reductionist model outputs, neglecting complex patterns encoded in the data. These black-box AI models might provide accurate classifications or predictions, but with little justification and interpretative power. For instance, an AI might classify humans as sick/healthy, without distinguishing between nuances of medical conditions and/or without providing key reasons or consequential patterns that motivate the classification. These two limits must be resolved within next-generation human-centric AI approaches, which must account for the complex nature of information and knowledge as relative to human users.

This Special Issue aims to bring together quantitative, innovative research in this field. We are open to a variety of publication types, including reviews and theoretical papers, empirical research, computational modeling, and Big Data analysis. Submissions to this Special Issue should demonstrate how the application of information/knowledge networks can work in synergy with AI and build novel ways to account for and interpret the complexity of human-focused data.

Potential topics include, but are not limited to, the following:

  •     Interpretable AI for information processing;
  •     Models of network science and AI for understanding information flow;
  •     Models of knowledge construction and representation;
  •     Modeling exploration and exploitation processes over knowledge structure;
  •     Complex system approaches to knowledge/information modeling;
  •     Interpretable stance detection through network science;
  •     Network visualization of knowledge representation;
  •     High-order and/or feature-rich graph representation/analysis of social phenomena;
  •     Opinion dynamics modeling;
  •     Trustworthy social and sociable interaction;
  •     AI systems’ individual versus collective goals;
  •     Self-organized, socially distributed information processing in AI-based techno-social systems.

Dr. Massimo Stella
Dr. Giulio Rossetti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • interpretable AI
  • human-centric AI
  • information
  • complex networks
  • network science
  • knowledge modeling
  • data mining
  • intelligent systems

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Published Papers (6 papers)

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Research

19 pages, 279 KiB  
Article
Development of an Assessment Scale for Measurement of Usability and User Experience Characteristics of Bing Chat Conversational AI
by Goran Bubaš, Antonela Čižmešija and Andreja Kovačić
Future Internet 2024, 16(1), 4; https://doi.org/10.3390/fi16010004 - 23 Dec 2023
Cited by 5 | Viewed by 3554
Abstract
After the introduction of the ChatGPT conversational artificial intelligence (CAI) tool in November 2022, there has been a rapidly growing interest in the use of such tools in higher education. While the educational uses of some other information technology (IT) tools (including collaboration [...] Read more.
After the introduction of the ChatGPT conversational artificial intelligence (CAI) tool in November 2022, there has been a rapidly growing interest in the use of such tools in higher education. While the educational uses of some other information technology (IT) tools (including collaboration and communication tools, learning management systems, chatbots, and videoconferencing tools) have been frequently evaluated regarding technology acceptance and usability attributes of those technologies, similar evaluations of CAI tools and services like ChatGPT, Bing Chat, and Bard have only recently started to appear in the scholarly literature. In our study, we present a newly developed set of assessment scales that are related to the usability and user experiences of CAI tools when used by university students, as well as the results of evaluation of these assessment scales specifically regarding the CAI Bing Chat tool (i.e., Microsoft Copilot). The following scales were developed and evaluated using a convenience sample (N = 126) of higher education students: Perceived Usefulness, General Usability, Learnability, System Reliability, Visual Design and Navigation, Information Quality, Information Display, Cognitive Involvement, Design Appeal, Trust, Personification, Risk Perception, and Intention to Use. For most of the aforementioned scales, internal consistency (Cronbach alpha) was in the range from satisfactory to good, which implies their potential usefulness for further studies of related attributes of CAI tools. A stepwise linear regression revealed that the most influential predictors of Intention to Use Bing Chat (or ChatGPT) in the future were the usability variable Perceived Usefulness and two user experience variables—Trust and Design Appeal. Also, our study revealed that students’ perceptions of various specific usability and user experience characteristics of Bing Chat were predominantly positive. The evaluated assessment scales could be beneficial in further research that would include other CAI tools like ChatGPT/GPT-4 and Bard. Full article
(This article belongs to the Special Issue Information Networks with Human-Centric AI)
24 pages, 1214 KiB  
Article
Evaluating the Perceived Quality of Mobile Banking Applications in Croatia: An Empirical Study
by Tihomir Orehovački, Luka Blašković and Matej Kurevija
Future Internet 2023, 15(1), 8; https://doi.org/10.3390/fi15010008 - 26 Dec 2022
Cited by 4 | Viewed by 5817
Abstract
Mobile banking is nowadays a standard service provided by banks worldwide because it adds convenience for people. There is no more rushing to a bank or waiting in lines for a simple transaction that can be conducted from anywhere and at any time [...] Read more.
Mobile banking is nowadays a standard service provided by banks worldwide because it adds convenience for people. There is no more rushing to a bank or waiting in lines for a simple transaction that can be conducted from anywhere and at any time in the blink of an eye. To be consumed by a respective amount of bank clients regularly, mobile banking applications are required to be continuously improved and updated, be in line with recent security standards, and meet quality requirements. This paper tackles the perceived quality of mobile banking applications that are most commonly used in Croatia and has three objectives in that respect. The first one is to identify the extent to which pragmatic and hedonic dimensions of quality contribute to customers’ satisfaction and their behavioral intentions related to the continuous use of mobile banking applications. The second one is to determine if there are significant differences in the perceived quality between users of diverse mobile banking applications as well as between users who belong to different age groups. The last one is to uncover the advantages and disadvantages of evaluated mobile banking applications. For this purpose, an empirical study was carried out, during which data were collected with an online questionnaire. The sample was composed of 130 participants who are representative and regular users of mobile banking applications. The psychometric features of the proposed research model, which represents an interplay of perceived quality attributes, were tested using the partial least squares structural equation modeling (PLS-SEM) method. Differences in the perceived quality among different mobile banking applications and customers of various age groups were explored with Kruskal–Wallis tests. Pros and cons of mobile banking applications were identified with the help of descriptive statistics. Study findings indicate that, in the context of mobile banking applications used in Croatia, feedback quality and responsiveness contribute to the ease of use, usefulness is affected by both ease of use and efficiency, responsiveness has a significant impact on efficiency while ease of use, usefulness, and security of personal data are predictors of customers’ satisfaction which in turn influences their behavioral intentions. While no significant difference exists in the perceived quality of four examined mobile banking applications, we found a significant difference in the perceived quality among three age groups of users of mobile banking applications. The most commonly reported advantages of mobile banking applications were related to facets of their efficiency and usefulness, whereas their main drawback appeared to be the lack of features dealing with the personalization of offered services. The reported and discussed results of an empirical study can be used as a set of guidelines for future advances in the evaluation and design of mobile banking applications. Full article
(This article belongs to the Special Issue Information Networks with Human-Centric AI)
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18 pages, 1346 KiB  
Article
Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts
by Stefan Claus and Massimo Stella
Future Internet 2022, 14(10), 291; https://doi.org/10.3390/fi14100291 - 12 Oct 2022
Cited by 5 | Viewed by 2733
Abstract
The ability to spot key ideas, trends, and relationships between them in documents is key to financial services, such as banks and insurers. Identifying patterns across vast amounts of domain-specific reports is crucial for devising efficient and targeted supervisory plans, subsequently allocating limited [...] Read more.
The ability to spot key ideas, trends, and relationships between them in documents is key to financial services, such as banks and insurers. Identifying patterns across vast amounts of domain-specific reports is crucial for devising efficient and targeted supervisory plans, subsequently allocating limited resources where most needed. Today, insurance supervisory planning primarily relies on quantitative metrics based on numerical data (e.g., solvency financial returns). The purpose of this work is to assess whether Natural Language Processing (NLP) and cognitive networks can highlight events and relationships of relevance for regulators that supervise the insurance market, replacing human coding of information with automatic text analysis. To this aim, this work introduces a dataset of NIDT=829 investor transcripts from Bloomberg and explores/tunes 3 NLP techniques: (1) keyword extraction enhanced by cognitive network analysis; (2) valence/sentiment analysis; and (3) topic modelling. Results highlight that keyword analysis, enriched by term frequency-inverse document frequency scores and semantic framing through cognitive networks, could detect events of relevance for the insurance system like cyber-attacks or the COVID-19 pandemic. Cognitive networks were found to highlight events that related to specific financial transitions: The semantic frame of “climate” grew in size by +538% between 2018 and 2020 and outlined an increased awareness that agents and insurers expressed towards climate change. A lexicon-based sentiment analysis achieved a Pearson’s correlation of ρ=0.16 (p<0.001,N=829) between sentiment levels and daily share prices. Although relatively weak, this finding indicates that insurance jargon is insightful to support risk supervision. Topic modelling is considered less amenable to support supervision, because of a lack of results’ stability and an intrinsic difficulty to interpret risk patterns. We discuss how these automatic methods could complement existing supervisory tools in supporting effective oversight of the insurance market. Full article
(This article belongs to the Special Issue Information Networks with Human-Centric AI)
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13 pages, 2365 KiB  
Article
Playful Meaning-Making as Prosocial Fun
by John M. Carroll, Fanlu Gui, Srishti Gupta and Tiffany Knearem
Future Internet 2022, 14(10), 288; https://doi.org/10.3390/fi14100288 - 30 Sep 2022
Viewed by 2220
Abstract
Smart city infrastructures enable the routine interleaving and integration of diverse activities, including new ways to play, to be playful, and to participate. We discuss three examples: (1) citizen-based water quality monitoring, which combines outdoor exercise and social interaction with safeguarding public water [...] Read more.
Smart city infrastructures enable the routine interleaving and integration of diverse activities, including new ways to play, to be playful, and to participate. We discuss three examples: (1) citizen-based water quality monitoring, which combines outdoor exercise and social interaction with safeguarding public water supplies, (2) a digital scavenger hunt, which combines the experiences of a community arts festival with shared reflections about significant community places and events, and (3) public thanking, which encourages people to acknowledge neighbors and local groups that serve and strengthen the community. Each of these interaction possibilities in itself alters lived experience modestly. We argue that lightweight and playful meaning making activities can be prosocial fun, that is to say, they can simultaneously be playful and fun, but also substantive contributions to the coherence and richness of a community. Full article
(This article belongs to the Special Issue Information Networks with Human-Centric AI)
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24 pages, 2162 KiB  
Article
FakeNewsLab: Experimental Study on Biases and Pitfalls Preventing Us from Distinguishing True from False News
by Giancarlo Ruffo and Alfonso Semeraro
Future Internet 2022, 14(10), 283; https://doi.org/10.3390/fi14100283 - 29 Sep 2022
Cited by 4 | Viewed by 2762
Abstract
Misinformation posting and spreading in social media is ignited by personal decisions on the truthfulness of news that may cause wide and deep cascades at a large scale in a fraction of minutes. When individuals are exposed to information, they usually take a [...] Read more.
Misinformation posting and spreading in social media is ignited by personal decisions on the truthfulness of news that may cause wide and deep cascades at a large scale in a fraction of minutes. When individuals are exposed to information, they usually take a few seconds to decide if the content (or the source) is reliable and whether to share it. Although the opportunity to verify the rumour is often just one click away, many users fail to make a correct evaluation. We studied this phenomenon with a web-based questionnaire that was compiled by 7298 different volunteers, where the participants were asked to mark 20 news items as true or false. Interestingly, false news is correctly identified more frequently than true news, but showing the full article instead of just the title, surprisingly, does not increase general accuracy. Additionally, displaying the original source of the news may contribute to misleading the user in some cases, while the genuine wisdom of the crowd can positively assist individuals’ ability to classify news correctly. Finally, participants whose browsing activity suggests a parallel fact-checking activity show better performance and declare themselves as young adults. This work highlights a series of pitfalls that can influence human annotators when building false news datasets, which in turn can fuel the research on the automated fake news detection; furthermore, these findings challenge the common rationale of AI that suggest users read the full article before re-sharing. Full article
(This article belongs to the Special Issue Information Networks with Human-Centric AI)
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13 pages, 495 KiB  
Article
Enriching Artificial Intelligence Explanations with Knowledge Fragments
by Jože Rožanec, Elena Trajkova, Inna Novalija, Patrik Zajec, Klemen Kenda, Blaž Fortuna and Dunja Mladenić
Future Internet 2022, 14(5), 134; https://doi.org/10.3390/fi14050134 - 29 Apr 2022
Cited by 8 | Viewed by 2993
Abstract
Artificial intelligence models are increasingly used in manufacturing to inform decision making. Responsible decision making requires accurate forecasts and an understanding of the models’ behavior. Furthermore, the insights into the models’ rationale can be enriched with domain knowledge. This research builds explanations considering [...] Read more.
Artificial intelligence models are increasingly used in manufacturing to inform decision making. Responsible decision making requires accurate forecasts and an understanding of the models’ behavior. Furthermore, the insights into the models’ rationale can be enriched with domain knowledge. This research builds explanations considering feature rankings for a particular forecast, enriching them with media news entries, datasets’ metadata, and entries from the Google knowledge graph. We compare two approaches (embeddings-based and semantic-based) on a real-world use case regarding demand forecasting. The embeddings-based approach measures the similarity between relevant concepts and retrieved media news entries and datasets’ metadata based on the word movers’ distance between embeddings. The semantic-based approach recourses to wikification and measures the Jaccard distance instead. The semantic-based approach leads to more diverse entries when displaying media events and more precise and diverse results regarding recommended datasets. We conclude that the explanations provided can be further improved with information regarding the purpose of potential actions that can be taken to influence demand and to provide “what-if” analysis capabilities. Full article
(This article belongs to the Special Issue Information Networks with Human-Centric AI)
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