The artificial intelligence divide: Who is the most vulnerable?

New Media & Society, Год журнала: 2024, Номер unknown

Опубликована: Фев. 26, 2024

This study investigates users’ artificial intelligence (AI)-related competencies (i.e., AI knowledge, skills, and attitudes) identifies the vulnerable user groups in AI-shaped online news entertainment environment. We surveyed 1088 Dutch citizens over age of 16 years identified five through latent class analysis: average users, expert advocates, skeptics, unskilled neutral unskilled. The most with lowest levels knowledge skills skeptics unskilled) were mostly older, lower education privacy protection than users. Overall, results this resonate existing findings on digital divide provide evidence for an emerging among Finally, societal implication is discussed, such as need programs applications explainable AI.

Язык: Английский

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Enabling explainable artificial intelligence capabilities in supply chain decision support making

Production Planning & Control, Год журнала: 2024, Номер unknown, С. 1 - 12

Опубликована: Фев. 27, 2024

Explainable artificial intelligence (XAI) has been instrumental in enabling the process of making informed decisions. The emergence various supply chain (SC) platforms modern times altered nature SC interactions, resulting a notable degree uncertainty. This study aims to conduct thorough analysis existing literature on decision support systems (DSSs) and their incorporation XAI functionalities within domain SC. Our revealed influence decision-making field utilizes SHapley Additive exPlanations (SHAP) technique online data using Python machine learning (ML) process. Explanatory algorithms are specifically crafted augment lucidity ML models by furnishing rationales for prognostications they produce. present establish measurable standards identifying constituents DSSs that context assessed prior research with regards ability make predictions, utilization dataset, number variables examined, development capability, validation decision-making, emphasizes domains necessitate additional exploration concerning intelligent under conditions

Язык: Английский

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Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

Artificial Intelligence Review, Год журнала: 2024, Номер 57(11)

Опубликована: Сен. 18, 2024

Abstract In recent years, Advanced Persistent Threat (APT) attacks on network systems have increased through sophisticated fraud tactics. Traditional Intrusion Detection Systems (IDSs) suffer from low detection accuracy, high false-positive rates, and difficulty identifying unknown such as remote-to-local (R2L) user-to-root (U2R) attacks. This paper addresses these challenges by providing a foundational discussion of APTs the limitations existing methods. It then pivots to explore novel integration deep learning techniques Explainable Artificial Intelligence (XAI) improve APT detection. aims fill gaps in current research thorough analysis how XAI methods, Shapley Additive Explanations (SHAP) Local Interpretable Model-agnostic (LIME), can make black-box models more transparent interpretable. The objective is demonstrate necessity explainability propose solutions that enhance trustworthiness effectiveness models. offers critical approaches, highlights their strengths limitations, identifies open issues require further research. also suggests future directions combat evolving threats, paving way for effective reliable cybersecurity solutions. Overall, this emphasizes importance enhancing performance systems.

Язык: Английский

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Responsible AI for Cities: A Case Study of GeoAI in African Informal Settlements

Journal of Urban Technology, Год журнала: 2025, Номер unknown, С. 1 - 27

Опубликована: Фев. 13, 2025

Язык: Английский

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Beyond the code: The impact of AI algorithm transparency signaling on user trust and relational satisfaction

Public Relations Review, Год журнала: 2024, Номер 50(5), С. 102507 - 102507

Опубликована: Сен. 25, 2024

Язык: Английский

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The role of explainable artificial intelligence (XAI) in innovation processes: a knowledge management perspective

Technology in Society, Год журнала: 2025, Номер unknown, С. 102909 - 102909

Опубликована: Апрель 1, 2025

Язык: Английский

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NFT-based identity management in metaverses: challenges and opportunities

SN Applied Sciences, Год журнала: 2023, Номер 5(10)

Опубликована: Сен. 8, 2023

Abstract A considerable number of people worldwide start their second lives in the digital world soon. The 3D Internet reflects world. Metaverse, most famous example Internet, is very popular and practical people’s daily lives. However, combining Metaverse with newly-emerging technologies (e.g., blockchain) provides new user-friendly features such as autonomy, accessibility, removing central authorities, etc. Despite mentioned attractive features, blockchain-based metaverses suffer various challenges, one user multiple identities, certificate issuing for users authentication-related issues, arresting malicious users. Generally, identity management a distributed environment where no authority exists challenging issue. This study focuses on challenge Metaverses to strike balance between users’ privacy regulation. proposes use Non-Fungible Tokens (NFTs) tool managing identities metaverses, they are considered an excellent choice this purpose. In addition explaining importance idea, paper identifies its including management, authentication security aspects. It then possible solutions using cryptographic tools). existing there many opportunities popularization relying blockchain technology, emerging Metaverse-related jobs, in-Metaverse investments huge revenues, applying twins provide realistic senses. also highlights critical role artificial intelligence (AI) metaverses.

Язык: Английский

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Revealing the influence of AI and its interfaces on job candidates' honest and deceptive impression management in asynchronous video interviews

Technological Forecasting and Social Change, Год журнала: 2023, Номер 198, С. 123011 - 123011

Опубликована: Ноя. 27, 2023

Язык: Английский

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Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

Industrial & Engineering Chemistry Research, Год журнала: 2024, Номер 63(2), С. 921 - 929

Опубликована: Янв. 6, 2024

Wastewater treatment, especially the efficient degradation of contaminants such as m-cresol, remains a pivotal challenge. This study investigates application artificial neural networks (ANN) in predicting total organic carbon (TOC) removal rates from m-cresol-contaminated wastewater by using ultraviolet (UV)-Fenton oxidation process. Six key variables, namely, Fe2+ dosage, H2O2 catalyst quantity, reaction time, pH, and substrate concentration, were employed inputs to ANN model. Leveraging this multivariable input comprehensive data set, model projected maximum TOC rate 87.12%, validated an efficiency 86.26% achieved through experiments under derived optimal conditions: dosage at 16.09 mg/L, 1.40 quantity 0.11 g/L, time 29.80 min, initial pH 3.66, concentration 50 mg/L. Comparative analysis with other machine learning algorithms further revealed that notably outperformed linear regression, support vector random forest terms precision. work paves way for resource-optimized experimental designs, fostering real-time monitoring refining advanced process proficiency industrial applications.

Язык: Английский

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Using explainable AI to unravel classroom dialogue analysis: Effects of explanations on teachers' trust, technology acceptance and cognitive load

British Journal of Educational Technology, Год журнала: 2024, Номер 55(6), С. 2530 - 2556

Опубликована: Апрель 23, 2024

Abstract Deep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt valuable feedback on their teaching practices. However, these deep learning models often have intricate structures numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding analysis likely leads distrust underutilize AI‐powered models. To tackle this issue, we leveraged explainable AI unravel conducted an experiment evaluate the effects explanations. Fifty‐nine pre‐service were recruited randomly assigned either a treatment ( n = 30) or control 29) group. Initially, both groups learned analyse using without Subsequently, group received explanations, while continued receive only predictions. results demonstrated that in exhibited significantly higher levels trust technology acceptance for compared those Notably, there no significant differences cognitive load between two groups. Furthermore, expressed high satisfaction During interviews, they also elucidated how changed perceptions features attitudes towards This study is among pioneering works propose validate use address interpretability challenges within learning‐based context analysis. Practitioner notes What already known about topic Classroom recognized crucial element process. Researchers utilized techniques, particularly methods, dialogue. models, characterized by structures, function boxes, lacking ability transparent limitation can result harbouring underutilizing paper adds highlights importance incorporating approaches issues associated Through experimental study, demonstrates providing enhances teachers' increasing load. Teachers express provided AI. Implications practice and/or policy integration effectively challenge complex used analysing Intelligent systems designed benefit from advanced approaches, which offer users automated By enabling understand underlying rationale behind analysis, contribute fostering users.

Язык: Английский

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