Many have suggested that AI-based interventions could enhance learning by personalization, improving teacher effectiveness, or by optimizing educational processes. However, they could also have unintended or unexpected side-effects, such as undermining learning by enabling procrastination, or reducing social interaction by individualizing learning processes. Responsible scientific experiments are required to map both the potential benefits and the side-effects. Current procedures used to screen experiments by research ethics committees do not take the specific risks and dilemmas that AI poses into account. Previous studies identified sixteen conditions that can be used to judge whether trials with experimental technology are responsible. These conditions, however, were not yet translated into practical procedures, nor do they distinguish between the different types of AI applications and risk categories. This paper explores how those conditions could be further specified into procedures that could help facilitate and organize responsible experiments with AI, while differentiating for the different types of AI applications based on their level of automation. The four procedures that we propose are (1) A process of gradual testing (2) Risk- and side-effect detection (3) Explainability and severity, and (4) Democratic oversight. These procedures can be used by researchers and ethics committees to enable responsible experiment with AI interventions in educational settings. Implementation and compliance will require collaboration between researchers, industry, policy makers, and educational institutions.
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poster voor de EuSoMII Annual Meeting in Pisa, Italië in oktober 2023. PURPOSE & LEARNING OBJECTIVE Artificial Intelligence (AI) technologies are gaining popularity for their ability to autonomously perform tasks and mimic human reasoning [1, 2]. Especially within the medical industry, the implementation of AI solutions has seen an increasing pace [3]. However, the field of radiology is not yet transformed with the promised value of AI, as knowledge on the effective use and implementation of AI is falling behind due to a number of causes: 1) Reactive/passive modes of learning are dominant 2) Existing developments are fragmented 3) Lack of expertise and differing perspectives 4) Lack of effective learning space Learning communities can help overcome these problems and address the complexities that come with human-technology configurations [4]. As the impact of a technology is dependent on its social management and implementation processes [5], our research question then becomes: How do we design, configure, and manage a Learning Community to maximize the impact of AI solutions in medicine?
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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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