Ai and Human Decision-Making
A.Y. 2024/2025
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
Lesson period: year
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Lesson period
year
Course syllabus
Unit 1.
The evolution of human intelligence
Three key properties: productivity, systematicity, compositionality
The "peculiar" role of causal reasoning in human understanding
Thinking as building a simulation of the world
Automatic/reactive/implicit processes vs Voluntary/deliberative/explicit processes
Inductions: the laws of human learning and memory
Deduction: simulating possible worlds
Unit 2.
Single-agent human decision-making
Choice under certainty
Judgment under risk and uncertainty
Choice under risk and uncertainty
Prospect theory
Perceptual decision making
Human advice integration
Human-AI collaborative decision-making
Unit 3.
Introduction to Artificial Intelligence and Law
Definition of Artificial Intelligence: Principles, types, and applications
The legal dimension of AI: legal implications and regulations
Historical and contemporary examples of the intersection between AI and Law
Law and AI: Ethical Issues
Bias and discrimination in AI: identification and mitigation
Privacy and data security in the era of AI
Ethical considerations and responsibilities in the use of AI
AI in the Judicial System
Use of AI in courts and criminal investigations
AI in crime prediction and prevention: benefits and challenges
Risk of bias in AI systems used in the judiciary
Regulation of AI
Analysis of global laws and policies regarding AI
Balancing innovation and human rights protection
Future challenges of AI regulation
AI and the Future of Legal Work
AI in the legal industry: perspectives and implications
Use of AI in legal practices: virtual assistants, smart contracts, and legal prediction
Skills development for a legal future with AI
The evolution of human intelligence
Three key properties: productivity, systematicity, compositionality
The "peculiar" role of causal reasoning in human understanding
Thinking as building a simulation of the world
Automatic/reactive/implicit processes vs Voluntary/deliberative/explicit processes
Inductions: the laws of human learning and memory
Deduction: simulating possible worlds
Unit 2.
Single-agent human decision-making
Choice under certainty
Judgment under risk and uncertainty
Choice under risk and uncertainty
Prospect theory
Perceptual decision making
Human advice integration
Human-AI collaborative decision-making
Unit 3.
Introduction to Artificial Intelligence and Law
Definition of Artificial Intelligence: Principles, types, and applications
The legal dimension of AI: legal implications and regulations
Historical and contemporary examples of the intersection between AI and Law
Law and AI: Ethical Issues
Bias and discrimination in AI: identification and mitigation
Privacy and data security in the era of AI
Ethical considerations and responsibilities in the use of AI
AI in the Judicial System
Use of AI in courts and criminal investigations
AI in crime prediction and prevention: benefits and challenges
Risk of bias in AI systems used in the judiciary
Regulation of AI
Analysis of global laws and policies regarding AI
Balancing innovation and human rights protection
Future challenges of AI regulation
AI and the Future of Legal Work
AI in the legal industry: perspectives and implications
Use of AI in legal practices: virtual assistants, smart contracts, and legal prediction
Skills development for a legal future with AI
Prerequisites for admission
None
Teaching methods
Unit 1.
Lectures with interactive questions and discussions on human and LLM performance in some basic reasoning tasks. The lectures will not follow the textbooks, which are complementary study materials (students are better off by reading them before, rather than after, the module).
Unit 2.
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 3.
Lectures, short movies, classroom discussions, group work, and exercises. The lectures will not follow the textbooks, which are complementary study materials.
Lectures with interactive questions and discussions on human and LLM performance in some basic reasoning tasks. The lectures will not follow the textbooks, which are complementary study materials (students are better off by reading them before, rather than after, the module).
Unit 2.
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 3.
Lectures, short movies, classroom discussions, group work, and exercises. The lectures will not follow the textbooks, which are complementary study materials.
Teaching Resources
Unit 1.
Pearl, J, MacKenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books. Introduction and chapters 1, 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
Steels, L. (2021). Conceptual Foundations of Human-Centric AI (in Chetouani, M., Dignum, V., Lukowicz, P., Sierra, C. (eds), Human-centered Artificial Intelligence: advanced lectures, 2023, Springer). Whole chapter.
Further compulsory material will be made available by the teacher during the course.
Unit 2.
Chosen chapters from:
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° edition). Cambridge University Press.
Kahneman, 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.
Stefano Ricci, Andrea Rossetti (eds), LEGAL ISSUES IN INFORMATION SOCIETY, Giuffrè 2024.
Further compulsory material will be made available by the teacher during the course.
Pearl, J, MacKenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books. Introduction and chapters 1, 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
Steels, L. (2021). Conceptual Foundations of Human-Centric AI (in Chetouani, M., Dignum, V., Lukowicz, P., Sierra, C. (eds), Human-centered Artificial Intelligence: advanced lectures, 2023, Springer). Whole chapter.
Further compulsory material will be made available by the teacher during the course.
Unit 2.
Chosen chapters from:
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° edition). Cambridge University Press.
Kahneman, 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.
Stefano Ricci, Andrea Rossetti (eds), LEGAL ISSUES IN INFORMATION SOCIETY, Giuffrè 2024.
Further compulsory material will be made available by the teacher during the course.
Assessment methods and Criteria
Unit 1.
Written exam: open and closed choice questions. Students are strongly encouraged to take the assessment immediately at the end of the module.
Unit 2.
Written exam: open and closed choice questions. Students are strongly encouraged to take the assessment immediately at the end of the module.
Unit 3.
Written exam: open questions. Students are strongly encouraged to take the assessment immediately at the end of the module.
Written exam: open and closed choice questions. Students are strongly encouraged to take the assessment immediately at the end of the module.
Unit 2.
Written exam: open and closed choice questions. Students are strongly encouraged to take the assessment immediately at the end of the module.
Unit 3.
Written exam: open questions. Students are strongly encouraged to take the assessment immediately at the end of the module.
M-PSI/01 - GENERAL PSYCHOLOGY - University credits: 12
Lessons: 96 hours
Professors:
Cherubini Paolo, Reverberi Franco, Rossetti Andrea