Ai and Human-Decision Making
A.Y. 2025/2026
Learning objectives
Aim of the course are: a) to build a foundational knowledge of the main features of human learning, reasoning and decision making, their unique strengths and weaknesses compared to current AI, and how AI can support (vs endanger) them; b) the legal provisions, principles, and concepts that may shield individuals from the most severe dangers of the digital environment.
Expected learning outcomes
Unit 1
Knowledge and understanding
· Understand the generalities of human cognitive processes as interdependent functions evolved and adapted to a natural physical and social environment
· The multi-layered human intelligence: automatic vs controlled processes (both for learning and reasoning)
· What's hard for humans and easy for machines, what's easy for humans and hard for machines
Applying knowledge and understanding
· How AI can integrate and support human judgments
· How AI can mislead and endanger human judgments
Unit 2
Knowledge and understanding
· Understand the ideal standards of decision-making both in the individual and interactive context
· Understand why people fail to cope with ideal standards
· Heuristics in decision-making and associated biases
· Prospect theory and associated formal modeling of decision-making
· Understand how indirect suggestions can influence decisions (nudging)
· Human metacognitive abilities and their limits
· Optimal advice integration and human departures from optimality
Applying knowledge and understanding
· Determination of the optimal course of action in different contexts, with examples from clinical decision-making and economic decisions
· Analysis of the typical decision course of individuals, with critical analysis of their limits
· Analysis of human advice integration, with critical analysis of their limits
Unit 3
Knowledge and understanding
· Contextualized knowledge of foundational legal principles and concepts related to data law and technology regulation
· Insight into the challenges that digitization poses for the legal environment
· Knowledge of possible policy solutions to the future of data regulation and new technologies
Applying knowledge and understanding
· The ability to critically evaluate key concepts of data laws, including the EU General Data Protection Regulation (GDPR), the IP, the Data Act, and the Artificial Intelligence Act
Knowledge and understanding
· Understand the generalities of human cognitive processes as interdependent functions evolved and adapted to a natural physical and social environment
· The multi-layered human intelligence: automatic vs controlled processes (both for learning and reasoning)
· What's hard for humans and easy for machines, what's easy for humans and hard for machines
Applying knowledge and understanding
· How AI can integrate and support human judgments
· How AI can mislead and endanger human judgments
Unit 2
Knowledge and understanding
· Understand the ideal standards of decision-making both in the individual and interactive context
· Understand why people fail to cope with ideal standards
· Heuristics in decision-making and associated biases
· Prospect theory and associated formal modeling of decision-making
· Understand how indirect suggestions can influence decisions (nudging)
· Human metacognitive abilities and their limits
· Optimal advice integration and human departures from optimality
Applying knowledge and understanding
· Determination of the optimal course of action in different contexts, with examples from clinical decision-making and economic decisions
· Analysis of the typical decision course of individuals, with critical analysis of their limits
· Analysis of human advice integration, with critical analysis of their limits
Unit 3
Knowledge and understanding
· Contextualized knowledge of foundational legal principles and concepts related to data law and technology regulation
· Insight into the challenges that digitization poses for the legal environment
· Knowledge of possible policy solutions to the future of data regulation and new technologies
Applying knowledge and understanding
· The ability to critically evaluate key concepts of data laws, including the EU General Data Protection Regulation (GDPR), the IP, the Data Act, and the Artificial Intelligence Act
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
year
Course syllabus
● Unit 1.
○ The nature of intelligence, in general: biological and artificial
○ The fundamental principles of human intelligence and the inferential cycle of knowledge
■ Human implicit and explicit induction, with an emphasis on causal judgment
■ Human deduction, with an emphasis on the use of causal knowledge
○ Similarities and differences between Human intelligence and the surface behavior of LLM-based AI: knowing them for knowing when to trust them.
● Unit. 2.
○ Single agent human decision making
■ Choice under certainty
■ Judgment under risk and uncertainty
■ Choice under risk and uncertainty
■ Prospect theory, nudging and boosting
■ Perceptual decision making
○ Human multi-agent decision making
■ Strategic interaction
■ Social decision making
○ Human - AI collaborative decision making
● Unit 3.
1. Understanding Rules
- Different Types of Rules
- The Dynamics of Rule Application
- The Essentials of Required Rules
2. The Nature and Function of Rules
- The Role of Constitutive and Regulatory Rules
- The Rationality Behind Norms
- Legal Principles vs. Rules
- Challenges in Rule Terminology
3. Language and Rules
- The Boundaries of Common Language in Rule Contexts
4. Generalization in Rule Application
- General Rules and Their Prescriptive Nature
5. Theoretical Underpinnings of Rules
- The Conceptual Basis for Adhering to Rules
- The Craft of Rule Development and Standardization
6. The Structure of Rule Systems
- The Stratification of Rule Layers
7. Rules as Guides
- How Rules Serve as Justifications
8. The Influence of Rules
- The Origin and Power of Normative Influence
- How Rules Gain Significance and Impact
- The Uneven Distribution of Rule Authority
○ The nature of intelligence, in general: biological and artificial
○ The fundamental principles of human intelligence and the inferential cycle of knowledge
■ Human implicit and explicit induction, with an emphasis on causal judgment
■ Human deduction, with an emphasis on the use of causal knowledge
○ Similarities and differences between Human intelligence and the surface behavior of LLM-based AI: knowing them for knowing when to trust them.
● Unit. 2.
○ Single agent human decision making
■ Choice under certainty
■ Judgment under risk and uncertainty
■ Choice under risk and uncertainty
■ Prospect theory, nudging and boosting
■ Perceptual decision making
○ Human multi-agent decision making
■ Strategic interaction
■ Social decision making
○ Human - AI collaborative decision making
● Unit 3.
1. Understanding Rules
- Different Types of Rules
- The Dynamics of Rule Application
- The Essentials of Required Rules
2. The Nature and Function of Rules
- The Role of Constitutive and Regulatory Rules
- The Rationality Behind Norms
- Legal Principles vs. Rules
- Challenges in Rule Terminology
3. Language and Rules
- The Boundaries of Common Language in Rule Contexts
4. Generalization in Rule Application
- General Rules and Their Prescriptive Nature
5. Theoretical Underpinnings of Rules
- The Conceptual Basis for Adhering to Rules
- The Craft of Rule Development and Standardization
6. The Structure of Rule Systems
- The Stratification of Rule Layers
7. Rules as Guides
- How Rules Serve as Justifications
8. The Influence of Rules
- The Origin and Power of Normative Influence
- How Rules Gain Significance and Impact
- The Uneven Distribution of Rule Authority
Teaching methods
Unit 1. Students are required to study the materials discussed each week before the lectures. The lectures will unfold as discussions, explanations, and problem-solving tasks on some of the issues illustrated in the textbooks.
Unit 2. Flipped classroom & Problem-based learning. Students are required to study the materials discussed each week before the lectures. The lectures will unfold as discussions, explanations, and problem-solving tasks on some of the issues illustrated in the textbooks.
Unit 3. Lectures, short movies, classroom discussions, group work, and exercises. Smartphone apps that allow students to respond in real-time to open or closed questions will be used.
Unit 2. Flipped classroom & Problem-based learning. Students are required to study the materials discussed each week before the lectures. The lectures will unfold as discussions, explanations, and problem-solving tasks on some of the issues illustrated in the textbooks.
Unit 3. Lectures, short movies, classroom discussions, group work, and exercises. Smartphone apps that allow students to respond in real-time to open or closed questions will be used.
Teaching Resources
Unit 1.
Italian-reading students:
Cherubini, P. (2025). Intelligenze: Ragionamento umano e IA a confronto. Carocci. Capitoli dall'1 al 3.
Pearl, J, MacKenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books. Introduction and chapters 1, 2, 3, 4.
Non-Italian-reading students:
Lecture slides & videorecordings, plus:
Pearl, J, MacKenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books. Introduction and chapters 1, 2, 3, 4
Johnson-Laird, P (2006). How we reason. Oxford University Press: chapters 1 (introduction), 2, 3, 4, 5.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Strauss, and Giroux: introduction and chapters 1, 2, 3, 4, 5, 6.
Unit 2. Chosen chapters from (consult the course website one month before course starts for knowing which ones to study for the first week):
Angner, E. (2020). A Course in Behavioral Economics (Third edition.). London: Palgrave.
Hunink, M. G. M. (2014). Decision Making in Health and Medicine: Integrating Evidence and Values (2° edizione). Cambridge University Press.
Khaneman, D. (2011). Thinking, fast and slow. Farrar, Strauss, and Giroux.
Further compulsory material will be made available by the teacher during the course.
Unit 3. The primary text of reference, but not the only one, will be:
Frederick F. Schauer, Playing by the Rules: A Philosophical Examination of Rule-Based Decision-Making in Law and in Life (1991). To prepare for the exam, students will also need to be familiar with everything that will be published on the e-learning site during the course.
Italian-reading students:
Cherubini, P. (2025). Intelligenze: Ragionamento umano e IA a confronto. Carocci. Capitoli dall'1 al 3.
Pearl, J, MacKenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books. Introduction and chapters 1, 2, 3, 4.
Non-Italian-reading students:
Lecture slides & videorecordings, plus:
Pearl, J, MacKenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books. Introduction and chapters 1, 2, 3, 4
Johnson-Laird, P (2006). How we reason. Oxford University Press: chapters 1 (introduction), 2, 3, 4, 5.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Strauss, and Giroux: introduction and chapters 1, 2, 3, 4, 5, 6.
Unit 2. Chosen chapters from (consult the course website one month before course starts for knowing which ones to study for the first week):
Angner, E. (2020). A Course in Behavioral Economics (Third edition.). London: Palgrave.
Hunink, M. G. M. (2014). Decision Making in Health and Medicine: Integrating Evidence and Values (2° edizione). Cambridge University Press.
Khaneman, D. (2011). Thinking, fast and slow. Farrar, Strauss, and Giroux.
Further compulsory material will be made available by the teacher during the course.
Unit 3. The primary text of reference, but not the only one, will be:
Frederick F. Schauer, Playing by the Rules: A Philosophical Examination of Rule-Based Decision-Making in Law and in Life (1991). To prepare for the exam, students will also need to be familiar with everything that will be published on the e-learning site during the course.
Assessment methods and Criteria
Three written partial exams, one per unit. All of them include open and closed choice questions. One, two, or all three assessments can be taken in any available session, student choice, but students are strongly encouraged to take each assessment at the first session available after the end of each unit. The final mark will be registered after all the three partial assessments are passed.
Parte A e B
M-PSI/01 - GENERAL PSYCHOLOGY - University credits: 6
Lessons: 48 hours
Professors:
Cherubini Paolo, Reverberi Franco, Rossetti Andrea
Parte C
M-PSI/01 - GENERAL PSYCHOLOGY - University credits: 3
Lessons: 24 hours
Professors:
Cherubini Paolo, Reverberi Franco, Rossetti Andrea
Parte D
M-PSI/01 - GENERAL PSYCHOLOGY - University credits: 3
Lessons: 24 hours
Professors:
Cherubini Paolo, Reverberi Franco, Rossetti Andrea