Audit of AI and Machine Learning

As artificial intelligence (AI) and machine learning (ML) technologies increasingly influence various industries, auditing these systems becomes essential. Auditing AI and ML ensures they are fair, ethical, and secure, while assessing their transparency and compliance with organisational and regulatory standards. This process not only checks the technical accuracy but also the broader societal impacts, such as bias and privacy. As these technologies evolve, so must the auditing practices to ensure they meet ethical standards and organisational goals.

The objective of this training programme is to provide participants with the knowledge and skills required to effectively audit AI (Artificial Intelligence) and machine learning systems. Participants will gain an understanding of AI and machine learning technologies, their impact on organisations, and the associated risks and challenges. By the end of the training, participants will be equipped to assess the ethical implications of AI, audit AI systems, and provide recommendations for ensuring transparency, fairness, and accountability.

Introduction to AI and Machine Learning

Learning Outcome:

• Understand the basics of AI and machine learning technologies.

• Familiarise with the different types of AI systems and their applications.

• Gain insights into the ethical considerations and challenges associated with AI.

Description:

• Overview of AI and machine learning technologies and their impact on organisations.

• Introduction to the ethical dimensions of AI, including bias, privacy, and accountability.

• Examination of different types of AI systems, such as supervised learning, unsupervised learning, and reinforcement learning.

Case studies: Analysis of real-world AI applications and their ethical implications.

Audit Methodologies for AI Systems

Learning Outcome:

• Develop knowledge of auditing methodologies and best practices for AI systems.

• Learn how to assess the transparency, fairness, and accountability of AI algorithms.

• Understand the risks and challenges related to AI bias, data privacy, and security.

Description:

• Overview of auditing principles and methodologies for AI systems.

• Examination of the transparency and explainability of AI algorithms.

• Assessment of fairness and bias in AI systems, including algorithmic discrimination.

• Considerations for auditing data privacy and security in AI applications.

Case studies: Analysis of AI systems to identify ethical concerns and audit areas.

Ethical Audit of AI Systems and Case Studies

Learning Outcome:

• Apply audit techniques and tools to evaluate the performance and reliability of AI systems.

• Develop skills to identify and mitigate ethical concerns in AI applications.

• Analyse case studies to identify potential improvements and best practices for AI systems.

Description:

• Practical techniques for auditing the performance and reliability of AI systems.

• Ethical considerations in auditing AI algorithms and decision-making processes.

• Strategies for identifying and mitigating AI bias and discrimination.

• Incorporating fairness, accountability, and transparency in AI audits.

Case studies: Participants will analyse real-world AI applications to identify ethical concerns, audit areas, and propose recommendations.

Detailed Algorithm Analysis and Continuous Auditing Practices

Learning Outcome:

• Deepen understanding of algorithmic functions and structures in the context of continuous auditing.

• Master the use of practical examples to elucidate the integration of continuous auditing techniques with AI systems.

• Develop the ability to perform dynamic audits that adapt to algorithmic changes over time.

Description:

• Detailed breakdown of common algorithms used in AI applications, focusing on their design, implementation, and operational challenges.

• Introduction to continuous auditing concepts: Definitions, importance, and benefits in a rapidly changing technological landscape.

• Practical examples of how continuous auditing is applied in real-world scenarios, emphasising automated systems and live data monitoring.

• Techniques for setting up continuous audit parameters, including triggers for reviews based on algorithmic outputs and behavioral patterns.

Case studies: Participants will engage in hands-on analysis of ongoing audit processes, focusing on the adjustment of audit strategies in response to findings and evolving AI functionalities.

Experienced Internal Auditors, Risk Managers, Compliance Officers, Technology Auditors, IT governance professionals, Data privacy professionals, Business and technology executives.