Artificial intelligence training courses

This course will provide you with an understanding of the main concepts of AI and AI systems to complement your knowledge of management system standards.

This course will provide you with 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).

This course will provide you with the understanding and tools to conduct an AI system impact assessment, either in isolation or as part of an ISO/IEC 42001:12023 AI management system.

Obtain an understanding of the key terms, definitions and concepts of an AI systems impact assessment based upon ISO/IEC DIS 42005. Developed by top subject matter experts, this course contains the same high-quality content you will find in our tutor-led training, but with the added benefit of being able to learn at your own pace and at any time.

Artificial intelligence (AI) is critically important to both you and your interested parties. BSI has developed a short session that makes you aware of what AI is, why it is important, and how your organization manages the threat. It also considers how you are a vital piece of the jigsaw and what you can do to help your organization.

This course will help you 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. You’ll learn to interpret and apply the key concepts and principles of the standard to existing processes within your organization.

This course will help you 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. You’ll learn to interpret and apply the key concepts and principles of the standard to existing processes within your organization.

This course will provide you an understanding of effective AI management, by using a systematic framework to protect the confidentiality, integrity and availability of your information and that of your interested parties.

This course will provide you an understanding of effective artificial intelligence management, by using a systematic framework to protect the confidentiality, integrity and availability of your information and that of your interested parties.

This course provides you with an oversight into how you can implement these controls if they are necessary for the effective implementation of your AIMS.

This course develops the necessary skills to assess and report on the conformance and implementation of an Artificial Intelligence Management System conforming to ISO/IEC 42001. You will learn how to initiate an audit, prepare and conduct audit activities, compile and distribute audit reports and complete follow-up activities.

This five-day intensive course trains AIMS auditors to lead, plan, manage and implement an Audit Plan. Provide knowledge and skills required to perform first, second and third-party AIMS audits against ISO/IEC 42001; in accordance with ISO 19011 and ISO 17021, as applicable.

With the growth in the use of AI systems, the need to demonstrate how organizations use these technologies responsibly, ISO/IEC 42001:2023 is the worlds first AI management system.  By implementing and maintaining suitable controls, organizations are able to demonstrate this responsible use in an effective manner, taking into consideration the role they play in the AI system life cycle.

Artificial Intelligence (AI) is transforming industries by enhancing efficiency, enabling data-driven decisions, and creating personalized experiences at scale. It powers innovations across healthcare, finance, and education, solving complex problems and uncovering insights that were previously out of reach. This course introduces AI’s impact, potential, and the ethical considerations that come with this transformative technology and the role individuals and organizations have in ensuring its responsible and ethical use.

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 course is to educate participants of the organizational and regulatory importance risk management for AI Enterprises and how to build a such a system based on ISO/IEC 23894:2023.

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.

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.

With the growth in the use of AI systems, the need to demonstrate how organizations use these technologies responsibly, ISO/IEC 42001:2023 is the worlds first AI management system.  By implementing and maintaining suitable controls, organizations are able to demonstrate this responsible use in an effective manner, taking into consideration the role they play in the AI system life cycle.

Understand the meaning of robustness in AI systems - a key concept in the forthcoming EU AI regulation, and the different kinds of robustness issues in the development and deployment of deep learning systems.

This course aims to give you knowledge of the main technical concepts of ISO/IEC 24029-1:2021, by explaining the different methods through which robustness can be assessed, as well as more advanced concepts such as data perturbation and abstract interpretation.

The main focus of this course is to understand the general principles behind assessing the classification performance of an AI system, the different aspects that can affect classification performance, and a set of basic metrics that we can use to evaluate a classifier.

Explore the different available metrics for three different classification scenarios (binary, multi-class, and multilabel), the available statistical significance tests that are appropriate for classification, and some important computational complexity metrics.