Course: Trustworthy Artificial Intelligence
Credits: 5
Hours: about 20
Teachers: Luca Oneto <luca.oneto@unige.it>
Schedule: held in 2023 and will be held again in 2025
Where: TBD
Exam: Small presentation (max 30 min) on how the concepts presented in the course ca be used/extended during the student PhD.
Material: -
Course Description
Abstract:
It has been argued that Artificial Intelligence (AI) is experiencing a fast process of commodification. This characterisation is of interest for big IT companies, but it correctly reflects the current industrialization of AI. This phenomenon means that AI systems and products are reaching the society at large and, therefore, that societal issues related to the use of AI and Machine Learning (ML) cannot be ignored any longer. Designing ML models from this human-centered perspective means incorporating human-relevant requirements such as reliability, fairness, privacy, and interpretability, but also considering broad societal issues such as ethics and legislation. These are essential aspects to foster the acceptance of ML-based technologies, as well as to be able to comply with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters.
Teaching mode:
Theoretical lesson plus laboratories in Python using Google Colab https://colab.research.google.com/
Program:
Introduction to AI and ML
Trustworthy AI and ML
Reliable ML
Fair ML
Private ML
Interpretable/Explainable ML
References:
L. Oneto, et al. Towards learning trustworthily, automatically, and with guarantees on graphs: an overview. Neurocomputing, 2022
Winfield, A. F. et al. "Machine ethics: the design and governance of ethical AI and autonomous systems." Proceedings of the IEEE 107.3 (2019): 509-517.
Floridi, L. "Establishing the rules for building trustworthy AI." Nature Machine Intelligence 1.6 (2019): 261-262.
L. Oneto and S. Chiappa. Fairness in machine learning. Recent Trends in Learning From Data. Springer, 2020
Biggio, B. and Roli F. "Wild patterns: Ten years after the rise of adversarial machine learning." Pattern Recognition 84 (2018): 317-331.
Guidotti, R. et al. "A survey of methods for explaining black box models." ACM computing surveys (CSUR) 51.5 (2018): 1-42.
Liu, B. et al. "When machine learning meets privacy: A survey and outlook." ACM Computing Surveys (CSUR) 54.2 (2021): 1-36.