Impact of AI-focussed technologies on social and technical competencies for HR managers – A systematic review and research agenda

Technological Forecasting and Social Change, Journal Year: 2024, Volume and Issue: 202, P. 123301 - 123301

Published: March 5, 2024

Research on the application of Artificial Intelligence (AI)-based technologies in HRM domain has attracted significant scholarly attention. Yet, few studies have consolidated key trends adopting AI for HRM, especially managerial competencies required AI-based and identifying research directions HR managers, including development an AI-focused competency framework managers. A systematic literature review (SLR) bibliometrics analysis were conducted to identify current direction managers HRM. Several themes capabilities identified, utilizing Dynamic Capabilities View (DCV). The SLR identified applications various tools techniques functions, recruitment selection was one with broadest use applications. Managerial cognitive capability, human capital, social capital DCV considered initial coding categories under which are adoption This study utilized SLR, Bibliometric, directed content as three distinct but interrelated sets methodologies extracting novel insights into It highlights associated that need mapping its adoption.

Language: Английский

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AI-Augmented Brainwriting: Investigating the use of LLMs in group ideation

Published: May 11, 2024

The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects integrating LLMs into the process – divergence stage idea generation, and convergence evaluation selection ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM enhancement group process, evaluated generation resulted solution space. To assess potential using in we design engine compared it to ratings assigned by three expert six novice evaluators. Our findings suggest that could enhance both its outcome. also provide evidence can support evaluation. conclude discussing HCI education practice.

Language: Английский

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Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 35796 - 35812

Published: Jan. 1, 2024

The field of drug discovery has experienced a remarkable transformation with the advent artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI ML models are becoming more complex, there is growing need for transparency interpretability models. Explainable Artificial Intelligence (XAI) novel approach that addresses this issue provides interpretable understanding predictions made by In recent years, been an increasing interest in application XAI techniques to discovery. This review article comprehensive overview current state-of-the-art discovery, including various methods, their challenges limitations also covers target identification, compound design, toxicity prediction. Furthermore, suggests potential future research directions aims provide state its transform field.

Language: Английский

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Explainable AI in Healthcare Application

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 123 - 176

Published: Jan. 18, 2024

Given the inherent risks in medical decision-making, professionals carefully evaluate a patient's symptoms before arriving at plausible diagnosis. For AI to be widely accepted and useful technology, it must replicate human judgment interpretation abilities. XAI attempts describe data underlying black-box approach of deep learning (DL), machine (ML), natural language processing (NLP) that explain how judgments are made. This chapter provides survey most recent methods employed imaging related fields, categorizes lists types XAI, highlights used make topics more interpretable. Additionally, focuses on challenging issues applications guides development better deep-learning system explanations by applying principles analysis pictures text.

Language: Английский

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Unlocking the potential of AI: Enhancing consumer engagement in the beauty and cosmetic product purchases

Journal of Retailing and Consumer Services, Journal Year: 2024, Volume and Issue: 79, P. 103842 - 103842

Published: April 5, 2024

Language: Английский

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FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare

BMJ, Journal Year: 2025, Volume and Issue: unknown, P. e081554 - e081554

Published: Feb. 5, 2025

Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited clinical practice. This paper describes FUTURE-AI framework, which provides guidance development trustworthy tools healthcare. The Consortium was founded 2021 comprises 117 interdisciplinary experts from 50 countries representing all continents, including scientists, researchers, biomedical ethicists, social scientists. Over a two year period, guideline established through consensus based on six guiding principles—fairness, universality, traceability, usability, robustness, explainability. To operationalise set 30 best practices were defined, addressing technical, clinical, socioethical, legal dimensions. recommendations cover entire lifecycle healthcare AI, design, development, validation to regulation, deployment, monitoring.

Language: Английский

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A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 61829 - 61854

Published: Jan. 1, 2023

Over the decades, Artificial Intelligence (AI) and machine learning has become a transformative solution in many sectors, services, technology platforms wide range of applications, such as smart healthcare, financial, political, surveillance systems. In large amount data is generated about diverse aspects our life. Although utilizing AI real-world applications provides numerous opportunities for societies industries, it raises concerns regarding privacy. Data used an system are cleaned, integrated, processed throughout life cycle. Each these stages can introduce unique threats to individual's privacy have impact on ethical processing protection data. this paper, we examine risks different phases cycle review existing privacy-enhancing solutions. We four categories risk, including (i) risk identification, (ii) making inaccurate decision, (iii) non-transparency systems, (iv) non-compliance with regulations best practices. then examined potential each phase, evaluated concerns, reviewed technologies, requirements, process solutions countermeasure risks. also some policies need compliance available AI-based The main contribution survey examining challenges solutions, technology, process, legislation entire phase cycle, open been identified.

Language: Английский

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Call for the responsible artificial intelligence in the healthcare

BMJ Health & Care Informatics, Journal Year: 2023, Volume and Issue: 30(1), P. e100920 - e100920

Published: Dec. 1, 2023

The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities potentials AI in healthcare, emphasising necessity explainability, trustworthiness, usability, transparency fairness developing implementing models. It underscores 'black box' challenge, highlighting gap between algorithmic outputs human interpretability, articulates pivotal role explainable enhancing accountability applications healthcare. discourse extends ethical considerations, exploring biases dilemmas that may arise application, a keen focus on ensuring equitable use across diverse global regions. Furthermore, explores concept responsible advocating for balanced approach leverages AI's capabilities enhanced delivery ensures ethical, transparent accountable technology, particularly clinical decision-making care.

Language: Английский

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An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews

Technological Forecasting and Social Change, Journal Year: 2024, Volume and Issue: 202, P. 123326 - 123326

Published: March 16, 2024

Sentiment analysis has demonstrated its value in a range of high-stakes domains. From financial markets to supply chain management, logistics, and technology legitimacy assessment, sentiment offers insights into public sentiment, actionable data, improved decision forecasting. This study contributes this growing body research by offering novel multi-view deep learning approach that incorporates non-textual features like emojis. The proposed considers both textual emoji views as distinct emotional information for the classification model, results acknowledge their individual combined contributions analysis. Comparative with baseline classifiers reveals incorporating significantly enriches analysis, enhancing accuracy, F1-score, execution time model. Additionally, employs LIME explainable provide model's decision-making process, enabling businesses understand factors driving customer sentiment. present literature on text context social media provides an innovative analytics method extract valuable from electronic word mouth (eWOM), which can help them stay ahead competition rapidly evolving digital landscape. In addition, findings paper have important implications policy development communication monitoring. Recognizing importance emojis expression inform policies helping better tailor solutions address concerns public.

Language: Английский

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Enabling explainable artificial intelligence capabilities in supply chain decision support making

Production Planning & Control, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 12

Published: Feb. 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

Language: Английский

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