Course: Trustworthy Artificial Intelligence
Hours: about 20
Teachers: Luca Oneto <firstname.lastname@example.org>
Schedule: from Monday 10th of July 2023 to Friday 14th of July 2023 from 8:00 to 12:00 CEST
Exam: Small presentation (max 30 min) on how the concepts presented in the course ca be used/extended during the student PhD.
It has been argued that Artificial Intelligence (AI) is experiencing a fast process of commodification. This characterization 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.
Reliable AI: Sensitivity Analysis, Robustness, Non-Regressivity, and Adversarial Machine Learning;
Fair AI: from Pre-, In-, and Post-Processing Models to Learn Fair Representations;
Private AI: Anonymization, Federated Learning, Differential Privacy, Homomorphic Encryption;
Interpretable/Explainable AI: making models more understandable.
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.