ISO 42001:2023 Training Courses

Identify the structure and requirements of an effective AIMS and what this means for you. Gain a thorough understanding of the history and development of ISO 42001:2023, key terms, definitions and the ISO harmonized structure.

AI can help position your place in the market, but there is a need to ensure this is done responsibly. Having an internationally recognized Artificial Intelligence Management System (AIMS) allows you to enhance organizational performance and gain a competitive edge when using AI.

To obtain an understanding of the key terms, definitions, and concepts of AI and AI systems, based upon ISO/IEC 22989:2023, to help with the management of an ISO/IEC 42001:2023 AI Management System (AIMS). 

The aim of this course is to introduce participants to the organizational and regulatory importance of ISO/IEC 23894:2023 as a risk management standard. This standard will most likely become harmonized against the AI Act post publication. 

The aim of this on-demand training is to provide participants with a comprehensive understanding of the factors that can impact the trustworthiness of an AI system, and equip them with the knowledge and skills necessary to ensure and improve trust, transparency, and ethical practices in AI development and deployment. 

The aim of this course is to give you the technical tools to ensure that your deep learning models are robust, i.e., they perform well all circumstances, even in unforeseen and unusual situations.  

We aim to give you knowledge of the main technical concepts of ISO/IEC 24029-1:2021, by explaining the different methods (statistical, formal, empirical) through which robustness can be assessed, and some more advanced concepts, such as data perturbation and abstract interpretation. 

The course helps reinforce the importance of assessing robustness and understanding the impact that robustness issues can have on the development and deployment of deep learning systems 

We aim to give you knowledge of the different contexts in which robustness concerns can arise, by providing examples of robustness concerns and give possible strategies for how to detect (and possibly address) such issues.

The goal of this training is to provide a general overview of the main tools at our disposal to evaluate performance in the context of a classification task. Going through the training will provide the learner with an in-depth understanding of the measures and metrics that are available to quantify performance, in addition to the main aspects that can be used as control criteria for this purpose. 

The goal of this training is to provide further detail of the main tools at our disposal to evaluate performance in the context of a classification task. Going through the training will provide the learner with an in-depth understanding of the measures and metrics that are available to quantify performance, in addition to the main aspects that can be used as control criteria for this purpose. 

The aim of this course is to give you the technical tools to ensure that your AI systems are unbiased, i.e., their decision-making process is ethical and fair against all potential societal groups. 

We aim to give you knowledge of the main technical concepts of ISO/IEC 24027:2021, by exploring measurement techniques and methods that can be used to assess bias, with the ultimate objective of mitigating bias-related vulnerabilities. It's important to note that all phases of the AI system lifecycle, including data collection, training, continual learning, design, testing, evaluation, and monitor, are within the scope of this standard. 

The course helps reinforce the importance of identifying various sources of bias and understanding what impact these can have on the development and deployment of AI systems.

We aim to give you knowledge of the different contexts in which bias concerns can arise, by providing examples of bias concerns and give possible strategies for how to detect (and possibly address) such issues. 

The objective of this course is to raise awareness about bias in the context of AI systems. We will explore into the potential risks and dangers associated with biased systems, and provide methods to assess and mitigate unwanted bias, in accordance with ISO/IEC 24027:2021. Through this course, participants will gain a deeper understanding of the concept of bias and learn practical approaches to evaluate and address bias in AI systems. Our aim is to empower individuals with the knowledge and tools needed to promote fairness and mitigate the impact of bias in AI systems. 

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