Lecture 1 – Introduction / What is AI?
01 - What is AI?
‘’AI refers to computer systems that perform tasks typically requiring
human intelligence, such as reasoning, learning, problem-solving, and
perception.’’
Key types of AI
- Narrow AI -> designed for one specific task (e.g. image recognition,
spam filters)
- General AI -> Hypothetical – matches full human cognitive ability;
not yet achieved
- Machine learning -> systems that learn patterns from data without
explicit programming
- Generative AI -> creates new content (text, images, code) – e.g.
ChatGPT
How machine learning works
1. Data input: large datasets are fed to the system
2. Pattern recognition: algorithms identify patterns in the data
3. Model training: the system adjusts to improve accuracy
4. Prediction/output: model applies learned patterns to new data
02 - Why AI matters for society
Economy & work
- Automation of routine tasks across industries
- New job categories and skill demands
- Productivity gains, but also displacement risks
Social & democratic life
- Personalized information
- AI in policing, justice, and welfare systems
- Changing human relationships and communication
Health & science
- AI accelerates drug discovery and diagnostics
- Personalized medicine and treatment plans
- Climate modelling and scientific research
Global power
, - AI as a geopolitical asset (US, China, EU race)
- Unequal access between nations and communities
- New forms of economic dependency
03 - Key ethical challenges
Bias & discrimination
- AI systems trained on historical data can replicate and amplify
existing inequalities — in hiring, lending, criminal justice, and
healthcare.
Privacy & surveillance
- Mass data collection and facial recognition enable unprecedented
monitoring of individuals, with limited accountability.
Autonomy & manipulation
- Recommendation algorithms can exploit psychological
vulnerabilities, shaping beliefs, emotions, and choices without users'
awareness.
Accountability & transparency
- When AI makes consequential decisions, it is often unclear who is
responsible — and how to contest or appeal the outcome.
Safety & control
- As AI systems become more capable and autonomous, ensuring they
behave safely and remain under human oversight becomes critical.
Global inequality
- AI's benefits concentrate in wealthy nations and corporations, risk
widening the gap between the Global North and South.
04 - How can we govern AI?
Regulation
- Hard rules set by government
Self-regulation
- Tech companies commit voluntarily to safety standards, ethics
boards, or codes of conduct – critics say this lacks enforcement
Technical standards
, - International bodies develop shared technical standards for safety,
transparency, and interoperability
Core principles of AI governance
- Transparency: AI systems should be explainable and auditable
- Accountability: clear lines of responsibility for AI decisions
- Fairness: non-discriminatory and equitable outcomes
- Human oversight: humans remain in control of consequential
decisions
All of the above was generated by AI -> why?
- Its correct and relevant
- It shows the transformation of both AI itself and our relation to it
o Can meaningfully take on part of our work
Lecture outline
1. AI, you and me
2. Structure of course/housekeeping
3. What is AI? How does AI work?
4. Is AI a force for good?
AI: up close and personal
How do I use AI?
- Brainstorming
- Feedback
- Technical helpdesk
- Tool design
- Text annotation
- For fun
AI: up close and personal
- AI is changing our lives and our work
- We need to find a personal balance and position
o Where does AI add value?
o Where does AI detract or deistrct?
, o How do we monitor and regulate this?
- We need to find a balance as a university & society
o How does AI change our teaching & learning?
o How does AI change our government?
o How does AI change our healthcare, military, etc.
o How do we monitor and regulate this?
What is AI? (1) Machine learning
- Detection of systematic patterns between input and output
- General task: predict output given specific features of the inputs
- Very similar to ‘’regular’’ statistical modeling
o Input features: independent variables
o Output class: dependent variables
- Key difference to statistical modeling:
o We care about predicting something, not about understanding
a (causal) process
o Models are highly complex (and multicollinear) and generally
seen as a black box
Black box = system that produces results without user
being able to see or understand how it works
Deep learning
- Fancy term for machine learning with very large models
- Based on:
o Very large neural networks (with a specific structure)
o Trained on enormous amounts of data, e.g. ‘’all of the
internet’’
o Using massive computing power, especially GPU’s
01 - What is AI?
‘’AI refers to computer systems that perform tasks typically requiring
human intelligence, such as reasoning, learning, problem-solving, and
perception.’’
Key types of AI
- Narrow AI -> designed for one specific task (e.g. image recognition,
spam filters)
- General AI -> Hypothetical – matches full human cognitive ability;
not yet achieved
- Machine learning -> systems that learn patterns from data without
explicit programming
- Generative AI -> creates new content (text, images, code) – e.g.
ChatGPT
How machine learning works
1. Data input: large datasets are fed to the system
2. Pattern recognition: algorithms identify patterns in the data
3. Model training: the system adjusts to improve accuracy
4. Prediction/output: model applies learned patterns to new data
02 - Why AI matters for society
Economy & work
- Automation of routine tasks across industries
- New job categories and skill demands
- Productivity gains, but also displacement risks
Social & democratic life
- Personalized information
- AI in policing, justice, and welfare systems
- Changing human relationships and communication
Health & science
- AI accelerates drug discovery and diagnostics
- Personalized medicine and treatment plans
- Climate modelling and scientific research
Global power
, - AI as a geopolitical asset (US, China, EU race)
- Unequal access between nations and communities
- New forms of economic dependency
03 - Key ethical challenges
Bias & discrimination
- AI systems trained on historical data can replicate and amplify
existing inequalities — in hiring, lending, criminal justice, and
healthcare.
Privacy & surveillance
- Mass data collection and facial recognition enable unprecedented
monitoring of individuals, with limited accountability.
Autonomy & manipulation
- Recommendation algorithms can exploit psychological
vulnerabilities, shaping beliefs, emotions, and choices without users'
awareness.
Accountability & transparency
- When AI makes consequential decisions, it is often unclear who is
responsible — and how to contest or appeal the outcome.
Safety & control
- As AI systems become more capable and autonomous, ensuring they
behave safely and remain under human oversight becomes critical.
Global inequality
- AI's benefits concentrate in wealthy nations and corporations, risk
widening the gap between the Global North and South.
04 - How can we govern AI?
Regulation
- Hard rules set by government
Self-regulation
- Tech companies commit voluntarily to safety standards, ethics
boards, or codes of conduct – critics say this lacks enforcement
Technical standards
, - International bodies develop shared technical standards for safety,
transparency, and interoperability
Core principles of AI governance
- Transparency: AI systems should be explainable and auditable
- Accountability: clear lines of responsibility for AI decisions
- Fairness: non-discriminatory and equitable outcomes
- Human oversight: humans remain in control of consequential
decisions
All of the above was generated by AI -> why?
- Its correct and relevant
- It shows the transformation of both AI itself and our relation to it
o Can meaningfully take on part of our work
Lecture outline
1. AI, you and me
2. Structure of course/housekeeping
3. What is AI? How does AI work?
4. Is AI a force for good?
AI: up close and personal
How do I use AI?
- Brainstorming
- Feedback
- Technical helpdesk
- Tool design
- Text annotation
- For fun
AI: up close and personal
- AI is changing our lives and our work
- We need to find a personal balance and position
o Where does AI add value?
o Where does AI detract or deistrct?
, o How do we monitor and regulate this?
- We need to find a balance as a university & society
o How does AI change our teaching & learning?
o How does AI change our government?
o How does AI change our healthcare, military, etc.
o How do we monitor and regulate this?
What is AI? (1) Machine learning
- Detection of systematic patterns between input and output
- General task: predict output given specific features of the inputs
- Very similar to ‘’regular’’ statistical modeling
o Input features: independent variables
o Output class: dependent variables
- Key difference to statistical modeling:
o We care about predicting something, not about understanding
a (causal) process
o Models are highly complex (and multicollinear) and generally
seen as a black box
Black box = system that produces results without user
being able to see or understand how it works
Deep learning
- Fancy term for machine learning with very large models
- Based on:
o Very large neural networks (with a specific structure)
o Trained on enormous amounts of data, e.g. ‘’all of the
internet’’
o Using massive computing power, especially GPU’s
