Ai and Human Decision-Making
A.Y. 2024/2025
Learning objectives
Aim of the course are: a) to build a foundational, general knowledge of the main features of human reasoning and decision making, their weaknesses, and how these weaknesses are liabilities often exploited by unscrupulous actors in the digital environment; b) basic knowledge and skills on some artificial tools commonly used to describe human reasoning and decision making, and to support them; c) 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, at low-granularity, the generalities of human cognitive processes seen as interdependent functions evolved and adapted to a natural environment ("complex adaptive systems" approach)
Understand the logical properties of two styles of human reasoning, and some of their psychological underpinnings and weaknesses
Understands the dangers of a physical-world-adapted CAS in interaction with a man-made, virtual environment, with practical examples
Applying knowledge and understanding
How to use simple bayesian networks to describe common human inferences
how to recognize common "traps for mind and behavior" on the web
Unit 2
Knowledge and understanding
Contextualised knowledge of foundational legal principles and concepts related to data law and technology regulation.
Insight into the challenges that digitisation 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.
Unit 3
Knowledge and understanding
Understand the ideal standards of decision-making both in 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)
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
Use of professional software for building and visualizing decision trees
Knowledge and understanding
Understand, at low-granularity, the generalities of human cognitive processes seen as interdependent functions evolved and adapted to a natural environment ("complex adaptive systems" approach)
Understand the logical properties of two styles of human reasoning, and some of their psychological underpinnings and weaknesses
Understands the dangers of a physical-world-adapted CAS in interaction with a man-made, virtual environment, with practical examples
Applying knowledge and understanding
How to use simple bayesian networks to describe common human inferences
how to recognize common "traps for mind and behavior" on the web
Unit 2
Knowledge and understanding
Contextualised knowledge of foundational legal principles and concepts related to data law and technology regulation.
Insight into the challenges that digitisation 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.
Unit 3
Knowledge and understanding
Understand the ideal standards of decision-making both in 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)
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
Use of professional software for building and visualizing decision trees
Lesson period: year
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
M-PSI/01 - GENERAL PSYCHOLOGY - University credits: 12
Lessons: 96 hours
Professors:
Cherubini Paolo, Reverberi Franco, Rossetti Andrea