|Nombre del curso:||Machine Learning for Decision-Making Support in Public Sector High-Stakes Problems|
Diana Benavides Prado
|Horario:||08 de Junio al 25 de Junio|
|Versión PDF||Click Aquí|
“The objective of this course is that students learn how to design accurate, trustworthy and transparent Machine Learning tools for supporting decision making in high-stakes problems in the public sector. As a result of the knowledge obtained in this course, students should have an understanding of challenges typically faced while deploying Machine Learning tools, such as social licence, ethics, human-algorithm interaction, algorithmic fairness and interpretability, some of which are also applicable to the private sector. Student will learn to analyse and find mechanisms to overcome these challenges, and to use that knowledge for a careful and responsible design and deployment of these tools.
Students are expected to learn to:
- Identify challenges and elements to be considered while designing and deploying Machine Learning to support decision-making in high-stakes problems in the public sector.
- Construct, evaluate and validate candidate Machine Learning models that are accurate, trustworthy, ethical and transparent.
- Acknowledge and evaluate challenges such as algorithmic fairness, model interpretability, human-algorithm interaction, transparency and ethics when constructing Machine Learning solutions for decision making with impact on humans.”
Rhema Vaithianathan – Professor of Economics and Co-Director of the Centre for Social Data Analytics, at The University of Queensland (UQ), Australia – Auckland University of Technology, Auckland, NZ. https://csda.aut.ac.nz/
Diana Benavides Prado – Senior Research Fellow (Data Science)- Centre for Social Data Analytics, at The University of Queensland (UQ), Australia – Auckland University of Technology, Auckland, NZ