LTAT.06.027 Trustworthy AI
Every day, we rely on AI-made decisions. But how do we know which ones to trust? Discriminating, opaque, and vulnerable AI systems are a risk for their owners and users. Every business using AI must know how to make it trustworthy.
- Lectures: MOOC (Online-based)
- Course coordinator: Huber Flores
Length: Four-week program Target audience: Everyone working with AI products and services
Part I: Trustworthy AI in society and business
- The societal impact of AI
- AI’s ethical implications
- The dimensions of trustworthy AI
- How can trustworthy AI transform businesses?
- The step in building AI applications
Part II: Fairness and accountability
- Challenges in implementing fairness in AI
- Detecting and mitigating bias and unfairness
Part III: Explainability
- The need for explainable AI
- Types of explainable AI
- How reliable are XAI explanations?
Part IV: Resilience
- Challenges in AI performance and data integrity
- AI security and privacy attacks
- Mitigating security risks
- AI laws and regulations in EU
Part V: Implications
- Developing trustworthy AI in your organisation
- Interactive case: trustworthiness of application screening AI
- Looking to the future of trustworthy AI
Part VI: Advanced principles
Now you should be ready to take the next course with more technical pespectives, Advanced Trustworthy AI
Announcements
- Certificate for UT students and staff is available for free, please present your certificate of passing the course.
