Explainable Artificial Intelligence

Black box AI systems for automated decision-making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for the lack of transparency but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust, clarity and understanding.

Explainable AI addresses such challenges and for years different AI communities have studied such topics, leading to different definitions, evaluation protocols, motivations, and results. 

This (20 hours+10 hours of hands-on laboratory) course provides a reasoned introduction to the work of Explainable AI (XAI) to date. It surveys the literature with a focus on post-hoc and by-design approaches.  

We motivate the needs of XAI in real-world and large-scale applications, while presenting state-of-the-art techniques and best practices, as well as discussing the many open challenges. An XAI platform with a collection of many of the recently proposed algorithms will be presented on specific use cases and it will be possible to familiarize with some of the methods.

The course is organized as follows in three modules: i) an introductory one providing motivations, main concepts and main methods; ii) an advanced one where the students will actively participate to monograph topics with readings interleaved with interventions of international scholars working on the sector; iii) an hands-on module where the students will be introduced to the usage on XAI methods.

The period is between January 8th and February 13th: two lectures and a laboratory session for the week.

The course will also provide one open lab to support students for project execution (support for project execution) (10 hours) Didattica Integrativa

Scheduling: Monday: 14-16 Tuesday 11-13 Aula Bianchi Scienze, Tuesday 16-18 Aula Fermi, form January 8

1) Module1 (10 hours):  Crush course on XAI.

1.  Motivation for XAI: Why explanation and What is an explanation. The taxonomy of XAI methods for Machine Learnin
2. Overview post-hoc explanation methods
3. Overview of transparent by-design methods

2) Module2 (10 hours): Advanced Concepts

1. Counterfactual explanations
2.  Explaining by design – argumentation and knowledge graph –
3.  Explaining by design & Global Explainer: on the integration of symbolic and sub-symbolic
4.  Interactive XAI – the new research challenges in XAI
5.  Student seminars on advanced topics

3)  Module3 (10 hours):  Hands-on: on XAI methods.  (By Roberto Pellungrini) interleaved with Module1

1.       The students will be introduced to python library of XAI-Lib methods for tabular data (4h)       
2.      The students will be introduced to python library of XAI methods for images data (4h)
3.       The students will be introduced to some global explanation method (2h)

Exam:     Student seminars and project presentation