{"id":2039,"date":"2025-08-13T09:56:50","date_gmt":"2025-08-13T07:56:50","guid":{"rendered":"https:\/\/venturenet.no\/?post_type=tribe_events&p=2039"},"modified":"2025-08-15T12:21:48","modified_gmt":"2025-08-15T10:21:48","slug":"inn-the-loop-human-centered-artificial-intelligence","status":"publish","type":"tribe_events","link":"https:\/\/venturenet.no\/nb\/all\/inn-the-loop-human-centered-artificial-intelligence\/","title":{"rendered":"INN-the-Loop: Human-Centered Artificial Intelligence"},"content":{"rendered":"
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\n\t\t\n\t\t\tjanuar 12, 2024\t\t<\/span>\n\n\t\t\t\t\t\n\t\t\t\t @ \t\t\t<\/span>\n\t\t\t\n\t\t\t\t8:00 am\t\t\t<\/span>\n\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t – \t\t\t\t<\/span>\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\tmars 30, 2025\t\t\t\t<\/span>\n\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t @ \t\t\t\t\t<\/span>\n\t\t\t\t\t\n\t\t\t\t\t\t5:00 pm\t\t\t\t\t<\/span>\n\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\tCET<\/span>\n\t\t\t\t\t\t<\/p>\n<\/div>\n\n\n\n

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INN-the-Loop was a research project in 2024 managed by the University of Inland Norway (INN) with contributions from the Norwegian Computing Center (NR), SINTEF, NTNU SFI NORCICS (Norwegian Center for Cybersecurity in Critical Sectors), and VentureNet AS. The project was co-financed by the Research Council of Norway and Innlandet County through the Regional Research Fund (RFF) program.<\/p>\n\n\n\n

The INN-the-Loop project analysed limitations and risks related to ML, GenAI and Agentic AI. It identified significant risks and barriers to widespread adoption of AI in healthcare and other critical sectors that demand high standards of accuracy, safety, security, and privacy.<\/p>\n\n\n\n

The project builds on results from a research project in 2023 with several leading Norwegian research institutions and VentureNet AS, a Norwegian technology company specialising in data exchange automation. A key result from the 2023 project was a framework for Human-Guided Artificial Intelligence and Data Exchange (Human-GAIDE).<\/p>\n\n\n\n

Human-GAIDE was developed to address widespread challenges with data accuracy in AI models. AI and machine learning (ML) models are vulnerable to missing data, erroneous data, manipulation of data and systems, and AI bias, all of which can impact the accuracy of an AI-model. Error and cyber vulnerabilities can compromise the safety of AI-enabled systems and lead to undesirable outcomes, such as proliferation of manipulated and distorted information or medical device failure.<\/p>\n\n\n\n

The project developed a highly scalable methodology to design trustworthy AI-systems with dynamic HITL models for smart healthcare. This is applicable to several critical sectors with high requirements for safety, security and resilience.<\/p>\n\n\n

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