People tend to be hesitant toward algorithmic tools, and this aversion potentially affects how innovations in artificial intelligence (AI) are effectively implemented. Explanatory mechanisms for aversion are based on individual or structural issues but often lack reflection on real-world contexts. Our study addresses this gap through a mixed-method approach, analyzing seven cases of AI deployment and their public reception on social media and in news articles. Using the Contextual Integrity framework, we argue that most often it is not the AI technology that is perceived as problematic, but that processes related to transparency, consent, and lack of influence by individuals raise aversion. Future research into aversion should acknowledge that technologies cannot be extricated from their contexts if they aim to understand public perceptions of AI innovation.
LINK
Artificial Intelligence (AI) is increasingly shaping the way we work, live, and interact, leading to significant developments across various sectors of industry, including media, finance, business services, retail and education. In recent years, numerous high-level principles and guidelines for ‘responsible’ or ‘ethical’ AI have been formulated. However, these theoretical efforts often fall short when it comes to addressing the practical challenges of implementing AI in real-world contexts: Responsible Applied AI. The one-day workshop on Responsible Applied Artificial InTelligence (RAAIT) at HHAI 2024: Hybrid Human AI Systems for the Social Good in Malmö, Sweden, brought together researchers studying various dimensions of Responsible AI in practice.This was the second RAAIT workshop, following the first edition at the 2023 European Conference on Artificial Intelligence (ECAI) in Krakow, Poland.
MULTIFILE
Purpose: Artificial intelligence (AI) and automation are currently changing human life with a great implication in the communication field. This research focusses on understanding the current and growing impact of AI and automation in the role of communication professionals to identify what skills and training are needed to face its impacts leading to a recommendation. Design/methodology/approach: The research involves methodological triangulation, analysing and comparing data gathered from consulting with experts using the Delphi method, focus group with communication students, and literature review. Findings: Findings show that the likely impacts are on the one hand the enhancing of efficiency and productivity, as well as freeing communication professionals to focus on the creative side, strategy and analytical thinking, on the other hand, repetitive and low-level jobs could be lost, being higher position jobs or those involving creativity and decision making harder to automate. Two types of training are needed: to gather experience with the current AI and automated tools, and to focus on developing human qualities that AI cannot replicate. Originality/value: The outcomes of this research are valuable to help current and future communication practitioners, as well as organisations, to be one step ahead and survive the age AI and automation, being aware of its current and near-future impacts. The paper offers a list of recommended soft and technical skills, as well as training needed, categorizing them in low, medium and high priority.
Artificial Intelligence (AI) wordt realiteit. Slimme ICT-producten die diensten op maat leveren accelereren de digitalisering van de maatschappij. De grote innovaties van de komende jaren –zelfrijdende auto’s, spraakgestuurde virtuele assistenten, autodiagnose systemen, robots die autonoom complexe taken uitvoeren – zijn datagedreven en hebben een AI-component. Dit gaat de rol van professionals in alle domeinen, gezondheidzorg, bouwsector, financiële dienstverlening, maakindustrie, journalistiek, rechtspraak, etc., raken. ICT is niet meer volgend en ondersteunend (een ‘enabling’ technologie), maar de motor die de transformatie van de samenleving in gang zet. Grote bedrijven, overheidsinstanties, het MKB, en de vele startups in de Brainport regio zijn innovatieve datagedreven scenario’s volop aan het verkennen. Dit wordt nog eens versterkt door de democratisering van AI; machine learning en deep learning algoritmes zijn beschikbaar zowel in open source software als in Cloud oplossingen en zijn daarmee toegankelijk voor iedereen. Data science wordt ‘applied’ en verschuift van een PhD specialisme naar een HBO-vaardigheid. Het stadium waarin veel bedrijven nu verkeren is te omschrijven als: “Help, mijn AI-pilot is succesvol. Wat nu?” Deze aanvraag richt zich op het succesvol implementeren van AI binnen de context van softwareontwikkeling. De onderzoeksvraag van dit voorstel is: “Hoe kunnen we state-of-the-art data science methoden en technieken waardevol en verantwoord toepassen ten behoeve van deze slimme lerende ICT-producten?” De postdoc gaat fungeren als een linking pin tussen alle onderzoeksprojecten en opdrachten waarbij studenten ICT-producten met AI (machine learning, deep learning) ontwikkelen voor opdrachtgevers uit de praktijk. Door mee te kijken en mee te denken met de studenten kan de postdoc overzicht en inzicht creëren over alle cases heen. Als er overzicht is kan er daarna ook gestuurd worden op de uit te voeren cases om verschillende deelaspecten samen met de studenten te onderzoeken. Deliverables zijn rapporten, guidelines en frameworks voor praktijk en onderwijs, peer-reviewed artikelen en kennisdelingsevents.
Met huidige opleidings- en trainingsprogramma’s kan niet worden voldaan aan de groeiende vraag naar vakbekwame medewerkers op gebied van kunstmatige intelligentie (AI). Europa heeft daarom een innovatieve Europese AI-strategie nodig, die de bijscholing van werkenden kan versnellen om aan deze steeds toenemende vraag te voldoen.
Multiple sclerosis (MS) is a severe inflammatory condition of the central nervous system (CNS) affecting about 2.5 million people globally. It is more common in females, usually diagnosed in their 30s and 40s, and can shorten life expectancy by 5 to 10 years. While MS is rarely fatal; its effects on a person's life can be profound, which signifies comprehensive management and support. Most studies regarding MS focus on how lymphocytes and other immune cells are involved in the disease. However, little attention has been given to red blood cells (erythrocytes), which might also be important in developing MS. Artificial intelligence (AI) has shown significant potential in medical imaging for analyzing blood cells, enabling accurate and efficient diagnosis of various conditions through automated image analysis. The project aims to implement an AI pipeline based on Deep Learning (DL) algorithms (e.g., Transfer Learning approach) to classify MS and Healthy Blood cells.
