Ai and human decision-making

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.

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