Level: Professional (P)

The adoption of artificial intelligence (AI) and its impact on businesses, other organisations and society is growing. It is important that those working directly or indirectly with data and AI models understand the ethical considerations, address these within their projects, and encourage responsible AI innovation to prevent harmful and unfair consequences of AI.This course delivered over three mornings  provides participants with an understanding of fundamental concepts in designing, developing and deploying responsible machine learning and AI-based systems.

Learning Outcomes

• Understand the main components of ethical frameworks and principles for building responsible AI systems.
• Gain a fundamental insight into ethical challenges and solutions to overcoming challenges when building AI systems.
• Understand AI-specific risks to individual rights and freedoms and practical steps to mitigate, reduce or manage them. ​
• Understand fairness and non-discrimination in AI and explore best practices for implementing fairness into the AI lifecycle.
• Gain awareness of different types of biases that can manifest in data, identify potential sources of bias before training a model, and understand how to evaluate the fairness of model performance in population subgroups.
• Understand the role of AI governance, AI assurance, and the main legal & human rights issues related to AI systems.

Topics Covered

• Ethical frameworks and principles for building responsible AI systems
• Fairness in the stages of the machine learning lifecycle
• Understanding and mitigating fairness-related harms in AI
• Bias types, sources of bias and evaluating a model for bias
• Legal & Human Right Issues, AI Governance, and AI Assurance

Target Audience

The course is ideal for everyone interested in the ethical aspects surrounding the development of responsible AI systems. It is suitable for anyone working directly or indirectly with data and AI models (machine learning) including data, machine learning and AI practitioners (including data scientists, analysts and statisticians), data stewards, policymakers, students, and academics.

Assumed Knowledge

Experience in machine learning may be an advantage but not essential.

Delegates will be given a small amount of pre-course materials to help them prepare for the course.

Dr Georgina Cosma

Dr Georgina Cosma received a Ph.D. degree in computer science from the University of Warwick, U.K., in 2008. She is a Senior Lecturer in Data Science & AI at Loughborough University, UK. Dr Cosma has ample leadership and practical research experience in developing data science and AI applications for industry and healthcare organisations. She is currently leading research on the design, development and deployment of responsible AI solutions for healthcare, information retrieval, and engineering as part of various grants funded by industry and research organisations.