Inteligência Artificial Explicável Simbólica

Objectives

By the end of the course, students will: - Understand the importance of explainability and trust in AI systems. - Learn how symbolic and sub-symbolic methods contribute to explainability. - Explore techniques for building AI systems that are transparent and trustworthy. - Gain practical experience with tools for explainable AI.

Syllabus

Module 1: Foundations of Explainable AI 1. **Introduction to

Explainability** 2. Symbolic vs Sub-Symbolic Methods ### Module 2: Symbolic AI for Post-hoc Explainability 1. Symbolic Representations 2. Applications of Symbolic Explainability ### Module 3: Sub-symbolic AI and Self-Explanation 1. Challenges of Explaining Sub-symbolic Models 2. Techniques for Sub-symbolic Explainability ### Module 4: Integrating Symbolic and Sub-symbolic Methods 1. Hybrid AI Systems 2. Practical Tools for XAI

Prerequisites

Basic knowledge of programming and artificial intelligence is advisable

Cross Competence Component

Interpersonal skills in carrying out programming work in groups of 2 students Intrapersonal skills with study time management for project and exam Critical and innovative thinking in solving the problem proposed in the project statement Information and media literacy using computer search and planning tools as black boxes

Laboratorial Component

The development of the project requires a computer.

Programming And Computing Component

In the degree where this course is offered are assured the Computing and Programming components according to MEPP 21212

Ethical Principles

All members of a group are responsible for the group's work. In any assessment, every student shall honestly disclose any help received and sources used. In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.