About AI Ethics
AI systems now help decide who gets a loan, a job interview or medical attention, and
they write, draw and speak on our behalf. AI ethics asks how these systems should be
built and used so that they are fair, safe, accountable and respectful of the people they
affect. This course starts with Introduction to AI Ethics, which looks at
why the field matters and the real cases behind it, then
Ethical Foundations & Frameworks, the moral theories and principles
that the rest of the course builds on.
Next you will work through the core problems one at a time.
Bias & Fairness shows where unfair outcomes come from and how to
measure them, while Transparency & Explainability covers how to open
up black-box models. Privacy & Data Protection deals with personal
data and laws such as the GDPR, and Accountability & Governance asks
who answers for an AI system when it goes wrong.
The course then widens its view. AI Safety & Alignment looks at
robustness and misuse, and Generative AI, Misinformation & Copyright
covers deepfakes, hallucinations and training data disputes. Work, Inequality
& Society, Environmental Impact & Sustainability and
Surveillance, Military & Dual-Use AI look at what AI does to jobs,
the planet and civil liberties.
Finally, you will see how the rules are being written and how to act on them:
Regulation & Policy covers the EU AI Act and standards such as the
NIST AI Risk Management Framework, and Responsible AI in Practice turns
the ideas into habits for building, buying and deploying AI systems.
No technical background is needed, so the course suits students, developers, product
managers and anyone curious about the topic. The
Introduction to AI
course is a good place to start if you are new to the field. Modules can be read in any
order, but we recommend going in sequence, because later modules refer back to the
principles from the first two.
Content
13 modules
01
Introduction to AI Ethics
What AI ethics is, why it matters now, and real cases where AI systems caused harm, along with the key terms and the people affected.
AI Ethics
Case Studies
Stakeholders
02
Ethical Foundations & Frameworks
The main moral theories and the principles behind AI guidelines, such as beneficence, justice and autonomy, and how to apply them to hard cases.
Consequentialism
Deontology
Virtue Ethics
03
Bias & Fairness
Where bias in data and models comes from, how fairness is defined and measured, and the trade-offs between competing definitions.
Data Bias
Fairness Metrics
Mitigation
04
Transparency & Explainability
Why black-box models are a problem, how explainability methods work, and what documentation should disclose to users and regulators.
Explainability
Model Cards
Datasheets
05
Privacy & Data Protection
How AI systems collect and expose personal data, and the tools and laws that protect people, from consent to anonymization.
Consent
Differential Privacy
GDPR
06
Accountability & Governance
Who is responsible when an AI system fails: audits, impact assessments, human oversight and internal governance structures.
Audits
Impact Assessments
Oversight
07
AI Safety & Alignment
Robustness, misuse and the challenge of making systems do what people intend, from reward hacking to debates about long-term risk.
Robustness
Alignment
Misuse
08
Generative AI, Misinformation & Copyright
Deepfakes, synthetic media, hallucinations, training data and copyright disputes, and how harm can be detected and reduced.
Deepfakes
Hallucination
Copyright
09
Work, Inequality & Society
How automation changes jobs, who benefits and who is left behind, and the effects on access, power and the digital divide.
Automation
Inequality
Digital Divide
10
Environmental Impact & Sustainability
The energy, water and hardware costs of training and running models, and ways to measure and reduce them.
Energy Use
Carbon Footprint
Water
11
Surveillance, Military & Dual-Use AI
Facial recognition, predictive policing, autonomous weapons and other technologies that can serve both helpful and harmful ends.
Facial Recognition
Autonomous Weapons
Dual Use
12
Regulation & Policy
The EU AI Act, other national approaches and standards such as the NIST AI Risk Management Framework, and how regulation shapes practice.
EU AI Act
NIST AI RMF
Standards
13
Responsible AI in Practice
Putting ethics into the development lifecycle with risk assessments, red teaming, checklists and ongoing monitoring.
Red Teaming
Risk Assessment
Monitoring
What You Can Do After This Course
By the end of the curriculum you will have covered the full path from ethical
foundations to putting responsible AI into practice. Concretely, you will be able to:
Reason about right and wrong: apply consequentialist, rule-based and virtue-based thinking, and the principles in AI guidelines, to real cases.
Spot and measure bias: trace unfair outcomes back to data and design choices, and test models with fairness metrics.
Make systems transparent: choose explainability methods and document models with model cards and datasheets.
Protect privacy: apply consent, anonymization and differential privacy, and understand duties under laws such as the GDPR.
Assign accountability: run audits and impact assessments, and set up human oversight and governance that works.
Weigh the wider harms: assess safety, misinformation, labor, environmental and surveillance risks before a system ships.
Navigate regulation: read the EU AI Act and frameworks such as the NIST AI RMF, and know what they ask of you.
Show proof of skill: practical projects such as a bias audit, a model card and an AI impact assessment.