AIM4Mobility: AI Challenges and Ethical Considerations in Urban Mobility
With innovation comes responsibility.
Training Format
Self-paced course.
Duration
8 hours (considering theoretical and practical tasks)
Language
English
Certificate of completion
Upon course compeltion, each participant will recieve a diploma from the EIT Urban Mobility.
Who is the course addressed to?
What will the participants get from the course
Identify
key ethical principles relevant to AI use in urban mobility, including fairness, transparency, and human oversight;
Interpret
how legal and policy frameworks apply to mobility-focused AI systems.
Understand
the potential risks and unintended consequences of AI systems in regulatory or operational contexts;
MODULES
MOBILITY AND TRANSPORT MARKETSModule 1
In this module, you'll explore the fundamental structures and dynamics of various transport ecosystems.
Starting with an overview of road transport systems, you'll learn about the key stakeholders and infrastructures involved. The module also covers the challenges and operations of railway and guided transport systems, as well as the crucial roles of port, maritime, and inland waterway systems in national mobility networks.
By the end of the module, you'll have a deeper understanding of the various transport sectors and their interconnections.
A final assessment will review all the concepts covered in this course.
Starting with an overview of road transport systems, you'll learn about the key stakeholders and infrastructures involved. The module also covers the challenges and operations of railway and guided transport systems, as well as the crucial roles of port, maritime, and inland waterway systems in national mobility networks.
By the end of the module, you'll have a deeper understanding of the various transport sectors and their interconnections.
A final assessment will review all the concepts covered in this course.
AI IN MOBILITY AND TRANSPORT
Module 2
Module 2
In this module, you'll learn about the foundational concepts of artificial intelligence (AI), including machine learning, deep learning, and emerging technologies like generative AI.
The course focuses on AI’s diverse applications across different transport modes. You'll explore its role in road transport for route optimiSation and mobility services, its impact on autonomous vehicles and safety in passenger transport, and how AI transforms freight transport with autonomous trucks and smart fleet management.
Additionally, the module covers AI’s role in infrastructure management, shared mobility services, and its integration in railway, maritime, and waterway transport systems.
By the end, you'll understand how AI is revolutioniSing mobility and transport across multiple sectors. A final assessment will review all the key concepts covered in the course.
The course focuses on AI’s diverse applications across different transport modes. You'll explore its role in road transport for route optimiSation and mobility services, its impact on autonomous vehicles and safety in passenger transport, and how AI transforms freight transport with autonomous trucks and smart fleet management.
Additionally, the module covers AI’s role in infrastructure management, shared mobility services, and its integration in railway, maritime, and waterway transport systems.
By the end, you'll understand how AI is revolutioniSing mobility and transport across multiple sectors. A final assessment will review all the key concepts covered in the course.
BENEFITS, CHALLENGES, AND RISKS OF AI IN MOBILITY AND TRANSPORT MARKETSModule 3
In this module, you'll learn how AI enhances operational efficiency and safety in the transport sector, while also addressing the key challenges that come with its adoption. The course highlights the risks of AI, including cybersecurity vulnerabilities, bias in decision-making, and privacy concerns, all within the context of mobility and transport. By the end, you'll have a balanced understanding of the potential and pitfalls of AI in this industry. A final assessment will consolidate your learning across all topics discussed in the course.
PRINCIPLES FOR ETHICAL, TRANSPARENT, TRUSTWORTHY, AND RESPONSIBLE AI USEModule 4
In this module, you'll learn about the key principles that guide the ethical and responsible use of AI.
The module covers the importance of transparency and explainability in AI decision-making processes to build trust and accountability. You'll also explore the role of human oversight in AI systems to ensure decisions remain accountable and fair. The module emphasizes preventing bias in AI, safeguarding personal data, and ensuring robust cybersecurity measures.
Additionally, you'll learn how AI can support environmental sustainability and social inclusion, while adhering to legal frameworks like the RGPD and AI regulations.
By the end, you'll understand how to design and implement AI systems that prioritize ethics and user rights.
A final assessment will review all topics covered in the course.
The module covers the importance of transparency and explainability in AI decision-making processes to build trust and accountability. You'll also explore the role of human oversight in AI systems to ensure decisions remain accountable and fair. The module emphasizes preventing bias in AI, safeguarding personal data, and ensuring robust cybersecurity measures.
Additionally, you'll learn how AI can support environmental sustainability and social inclusion, while adhering to legal frameworks like the RGPD and AI regulations.
By the end, you'll understand how to design and implement AI systems that prioritize ethics and user rights.
A final assessment will review all topics covered in the course.
AN ETHICAL AND STRATEGIC APPROACH TO AIModule 5
In this module, you'll learn how to align AI development with ethical principles and organizational goals through strategic planning.
You'll explore how to set measurable, inclusive objectives; identify and assess risks; and ensure data quality for fair outcomes.
The course emphasizes the importance of accessibility, privacy protection, accountability, and effective human oversight in AI systems. You'll also examine strategies for enhancing human decision-making, securing AI systems against threats, and fostering transparency to build stakeholder trust.
A final assessment will help you consolidate all the concepts discussed in the course.
You'll explore how to set measurable, inclusive objectives; identify and assess risks; and ensure data quality for fair outcomes.
The course emphasizes the importance of accessibility, privacy protection, accountability, and effective human oversight in AI systems. You'll also examine strategies for enhancing human decision-making, securing AI systems against threats, and fostering transparency to build stakeholder trust.
A final assessment will help you consolidate all the concepts discussed in the course.
AIM4Mobility is made up for 4 courses
Tools and Techniques for Geospatial ML
This course teaches geospatial data analysis with Python to tackle urban mobility challenges, using OSM data and spatial tools.
Fundamental Trends of AI & ML
The couse covers applications like demand forecasting, traffic optimisation, and autonomous vehicles, as well as key tools, challenges, and ethical issues.
Predictive Modeling and Demand Forecasting
This course teaches how to build and apply predictive models in urban mobility using AI and ML.
AI Challenges & Ethical Considerations
Introduction to ethical principles for AI in urban mobility, covering oversight, accountability, data governance, and fairness.
This project is supported by EIT Urban Mobility, an initiative of the European Institute of Innovation and Technology (EIT), a body of the European Union. EIT Urban Mobility acts to accelerate positive change on mobility to make urban spaces more liveable. Learn more: eiturbanmobility.eu
