The Limitations of Machine Learning & Us.


Machine learning (ML), particularly deep learning, is being used everywhere. However, not always is used well, ethically and scientifically. In this talk we first do a deep dive in the limitations of supervised ML and data, its key component. We cover small data, datification, bias, predictive optimization issues, evaluating success instead of harm, and pseudoscience, among other problems.  The second part is about our own limitations using ML, including different types of human incompetence: cognitive biases, unethical applications, no administrative competence, copyright violations, misinformation, and the impact on mental health. In the final part we discuss regulation on the use of AI and responsible AI principles, that can mitigate the problems outlined above.

Date: Mar 25, 2024 at 15:30:00 h
Venue: Sala de Seminario John Von Neumann, CMM, Beauchef 851, Torre Norte, Piso 7.
Speaker: Ricardo Baeza-Yates
Affiliation: Institute for Experiential AI @ Northeastern University & DCC, Univ. de Chile.
Coordinator: José Verschae

Posted on Mar 20, 2024 in ACGO, Seminars