Level: Professional (P)
Delivered over five afternoons this course provides participants with an understanding of fundamental concepts in designing, developing and deploying responsible AI systems.
Please note: Bookings will close 4 working days before the course start date or when the course has reached its maximum capacity.
The course provides participants with an understanding of fundamental concepts in designing, developing and deploying responsible AI systems. The course introduces concepts around AI Ethics and Governance including principals and frameworks; provides an overview of fairness in AI and introduces tools to assess and mitigate AI harms; explores AI explainability concepts and techniques; and discusses legal and human right issues related to AI systems, and the role of AI governance and AI assurance. The course will provide a practical insight into how tools and techniques can be utilised to evaluate fairness and analyse fairness-related harms during the machine leaning lifecycle at the stages of task definition, data collection, model training and evaluation.
By the end of this course, participants will understand
- Understand the main components of ethical frameworks and principles for building responsible AI systems
- Gain a fundamental and practical insight into ethical challenges and utilise tools, techniques for overcoming challenges when building responsible AI systems
- Identify where and how to embed fairness in the stages of the lifecycle, and to apply statistical methods to evaluate fairness of machine learning models
- Detect and understand unfairness in AI, and propose solutions for mitigating fairness related harms
- Describe and apply AI Explainability tools and techniques, such as feature and permutation importance
- Understand the role of AI governance, AI assurance, and the main legal & human right issues related to AI systems
- 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
- AI Explainability tools and techniques
- Legal & Human Right Issues, AI Governance, AI Assurance
The course is ideal for everyone interested in the ethical aspects surrounding the development of responsible AI.
AI practitioners and Data Scientists who design, develop, deploy, use, and interact with AI systems. They may be employees from industry or other organisations, researchers, students who want to learn about concepts, tools and techniques around the design and development of fair and responsible AI systems.
Data and analytics professionals who are preparing and analysing data and would like to learn how to ensure that ethical, fair and responsible practices are adopted. They may include data scientists, data miners, data analysts, data engineers and data curators.
Professionals, managers, team leaders and other employees from organisations who are considering the integration of AI into their systems and wish to do this responsibly and fairly.
Academics and others who are interested in using AI technology to make a positive impact in society.
Experience in machine learning and Python may be an advantage but not essential, as the practical sessions will mainly comprise of Python tutorials to run and discuss.
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.
17 September 2022
17 September 2022
RSS CStat/Gradstat/Data Analyst
also MIS & FIS