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AIM4Mobility: AI Challenges and Ethical Considerations in Urban Mobility

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This course offers a practical introduction to ethical principles in AI for urban mobility, covering human oversight, accountability, data governance, and fairness. Learners will explore regulations and guidelines to assess AI use in line with legal and ethical standards.
PROFESSIONAL TRAINING COURSE

With innovation comes responsibility.

Artificial Intelligence is rapidly reshaping the way cities move—optimizing transport systems, improving safety, and enabling smarter infrastructure. But, with innovation comes responsibility.

"AI Challenges and Ethical Considerations in Urban Mobility" is a self-paced online course designed to help professionals navigate the ethical, legal, and societal implications of applying AI in urban mobility.
From human oversight to data governance and fairness, you’ll learn how to critically assess and implement AI systems aligned with public interest and regulatory frameworks.

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?

AIM4Mobility is ideal for urban planners, transportation engineers, data scientists, policymakers and professionals interested in applying AI and ML to mobility challenges.

What will the participants get from the course

By the end of this course, learners will be able to:

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

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AIM4Mobility is made up for 4 courses

Enrol in all four courses and get a +50% discount. More info >

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.

Want to learn more about the AIM4Mobility course?

Visit the main page of the course which explains the AIM4Mobility framework, format and detailed information of all four courses. 

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