This study provides a comprehensive analysis of the AI-related skills and roles needed to bridge the AI skills gap in Europe. Using a mixed-method research approach, this study investigated the most in-demand AI expertise areas and roles by surveying 409 organizations in Europe, analyzing 2,563 AI-related job advertisements, and conducting 24 focus group sessions with 145 industry and policy experts. The findings underscore the importance of both general technical skills in AI related to big data, machine learning and deep learning, cyber and data security, large language models as well as AI soft skills such as problemsolving and effective communication. This study sets the foundation for future research directions, emphasizing the importance of upskilling initiatives and the evolving nature of AI skills demand, contributing to an EU-wide strategy for future AI skills development.
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Whitepaper: The use of AI is on the rise in the financial sector. Utilizing machine learning algorithms to make decisions and predictions based on the available data can be highly valuable. AI offers benefits to both financial service providers and its customers by improving service and reducing costs. Examples of AI use cases in the financial sector are: identity verification in client onboarding, transaction data analysis, fraud detection in claims management, anti-money laundering monitoring, price differentiation in car insurance, automated analysis of legal documents, and the processing of loan applications.
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Multilevel models (MLMs) are increasingly deployed in industry across different functions. Applications usually result in binary classification within groups or hierarchies based on a set of input features. For transparent and ethical applications of such models, sound audit frameworks need to be developed. In this paper, an audit framework for technical assessment of regression MLMs is proposed. The focus is on three aspects: model, discrimination, and transparency & explainability. These aspects are subsequently divided into sub-aspects. Contributors, such as inter MLM-group fairness, feature contribution order, and aggregated feature contribution, are identified for each of these sub-aspects. To measure the performance of the contributors, the framework proposes a shortlist of KPIs, among others, intergroup individual fairness (DiffInd_MLM) across MLM-groups, probability unexplained (PUX) and percentage of incorrect feature signs (POIFS). A traffic light risk assessment method is furthermore coupled to these KPIs. For assessing transparency & explainability, different explainability methods (SHAP and LIME) are used, which are compared with a model intrinsic method using quantitative methods and machine learning modelling.Using an open-source dataset, a model is trained and tested and the KPIs are computed. It is demonstrated that popular explainability methods, such as SHAP and LIME, underperform in accuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution on the outcome). For other contributors, such as group fairness and their associated KPIs, similar analysis and calculations have been performed with the aim of adding profundity to the proposed audit framework. The framework is expected to assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit providers, users, and assessment bodies, as defined in the European Commission’s proposed Regulation on Artificial Intelligence, when deploying AI-systems such as MLMs, to be future-proof and aligned with the regulation.
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In het boek komen 40 experts aan het woord, die in duidelijke taal uitleggen wat AI is, en welke vragen, uitdagingen en kansen de technologie met zich meebrengt.
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Multilevel models using logistic regression (MLogRM) and random forest models (RFM) are increasingly deployed in industry for the purpose of binary classification. The European Commission’s proposed Artificial Intelligence Act (AIA) necessitates, under certain conditions, that application of such models is fair, transparent, and ethical, which consequently implies technical assessment of these models. This paper proposes and demonstrates an audit framework for technical assessment of RFMs and MLogRMs by focussing on model-, discrimination-, and transparency & explainability-related aspects. To measure these aspects 20 KPIs are proposed, which are paired to a traffic light risk assessment method. An open-source dataset is used to train a RFM and a MLogRM model and these KPIs are computed and compared with the traffic lights. The performance of popular explainability methods such as kernel- and tree-SHAP are assessed. The framework is expected to assist regulatory bodies in performing conformity assessments of binary classifiers and also benefits providers and users deploying such AI-systems to comply with the AIA.
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Digital surveillance technologies using artificial intelligence (AI) tools such as computer vision and facial recognition are becoming cheaper and easier to integrate into governance practices worldwide. Morocco serves as an example of how such technologies are becoming key tools of governance in authoritarian contexts. Based on qualitative fieldwork including semi-structured interviews, observation, and extensive desk reviews, this chapter focusses on the role played by AI-enhanced technology in urban surveillance and the control of migration between the Moroccan–Spanish borders. Two cross-cutting issues emerge: first, while international donors provide funding for urban and border surveillance projects, their role in enforcing transparency mechanisms in their implementation remains limited; second, Morocco’s existing legal framework hinders any kind of public oversight. Video surveillance is treated as the sole prerogative of the security apparatus, and so far public actors have avoided to engage directly with the topic. The lack of institutional oversight and public debate on the matter raise serious concerns on the extent to which the deployment of such technologies affects citizens’ rights. AI-enhanced surveillance is thus an intrinsically transnational challenge in which private interests of economic gain and public interests of national security collide with citizens’ human rights across the Global North/Global South divide.
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In the book, 40 experts speak, who explain in clear language what AI is, and what questions, challenges and opportunities the technology brings.
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Jo-An Kamp is a lecturer and researcher at Fontys University of Applied Sciences in the Netherlands. She coaches ICT students in the fields of UX, research, (interactive) media, communication, (interaction) design, ethics and innovation. She does research on the impact of technology on humans and society. Jo-An is co-creator of the Technology Impact Cycle Toolkit (www.tict.io), a toolkit designed to make people think and make better decisions about (the implementation of) technology and is a member of the Moral Design Strategy research group.
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Het tekort aan handjes in de accountancy neemt toe. Tegelijk groeit de maatschappelijk druk om bedrijven zo goed mogelijk te controleren, om te zorgen dat ze financieel, fiscaal en qua duurzaamheid in de pas blijven lopen met de (toenemende) regelgeving. Gelukkig komt er steeds meer technologie voorhanden die de accountant kan helpen bij het controleren van de boeken, schetst Eric Mantelaers, hoofd Bureau Vaktechniek, RSM Accountants. Medio juni is hij aan de Open Universiteit gepromoveerd op zijn proefschrift ‘An evaluation of technologies to improve auditing’.
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What is the ‘raison d’être’ of auditing? Does auditing only exist by the grace of the legislator? Or does auditing fulfill other needs in contemporary society? For many companies, auditing has been made mandatory. This is possibly one of the reasons why researchers to date have given limited attention regarding the drivers for the demand for audit. Auditing (seen as a social control mechanism) is part of an organizational order in society. Therefore, it is essential to reflect on the (changing) demands of society. As a lack of insight why society demands an audit, accommodates the risk of not meeting the needs and expectations of society, the added value of auditing may be called into question. This dissertation deals with the question: what are drivers for the demand for audit for Dutch SME companies which are not mandatory required to have their financial statements audited.
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