Designing Ethical and Trustworthy Intelligent Systems
As artificial intelligence continues to shape our world, ensuring that these systems are ethically sound, socially aligned, and scientifically robust is more important than ever. This course offers a comprehensive introduction to the principles and best practices of responsible AI development, equipping participants with the tools needed to create AI systems that are both effective and trustworthy.
Learners will explore the ethical foundations of AI, including fairness, accountability, and transparency, and understand how these principles translate into real-world development practices. A strong focus is placed on detecting and mitigating data bias, examining how even subtle imbalances in training data can lead to unintended consequences and unequal outcomes.
The course also highlights the importance of scientific and methodological rigour—essential for building reproducible, reliable models that uphold public confidence and meet emerging regulatory standards.
Ideal for developers, data scientists, project managers, and compliance professionals, this course empowers participants to take a proactive, values-driven approach to AI innovation.
Fees
COURSE OUTLINES
Learning Outcomes
By the end of this course, participants will be able to:
- Describe the principles of responsible AI and their relevance to organisational and societal contexts.
- Identify sources and manifestations of bias in AI datasets and models.
- Comprehend foundational methodologies for bias detection, measurement, and mitigation during the AI lifecycle.
- Understand the importance of reproducibility in AI research and development.
- Evaluate whether AI development practices meet accepted transparency, fairness, and scientific rigour standards.
- Promote governance structures that support responsible AI practices within organisations.
- Recognise the roles of diverse stakeholders (e.g., legal and technical, and social actors) in shaping ethical AI development.
COURSE DETAILS
Duration: 2 Days
Tutor: Dr. Graziella De Martino
Course Scheduling
These courses are scheduled based on demand and will run once the minimum number of 6 participants is reached. We accept a maximum of 15 participants per course to ensure quality and engagement.
Interested? Please email us on training@nouv.com for more information or to express your interest.
In-House Training Available
All our courses can be delivered in-house for your team or organisation. Whether you're looking to upskill a department or deliver a course across your organisation, this flexible option allows us to tailor content to your specific needs and deliver training at a time and location that works for you.
Contact us to discuss how we can support your team’s development goals or to arrange a session.
PREREQUISITES
Participants are expected to have a foundational understanding of artificial intelligence or machine learning concepts. Prior experience with data analysis, AI tools, or software development is recommended but not essential.