Welcome to our seminar class webpage! Below you will find some description of this course.
This seminar class will explore studies that tackle societal problems using machine learning (ML) and, more broadly, artificial intelligence (AI). You can find the course description below.
Course Description:
In recent decades, there has been an emerging line of research that seeks to tackle critical societal problems (e.g., The United Nations Sustainable Development Goals) using machine learning (ML) and, more broadly, artificial intelligence (AI) to potentially improve the well-being of the society. For example, some of these problems seek to address poverty, (poor) education, gender inequality, peace/justice, and climate change.
This emerging line of research can be referred to as AI for Social Good (AI4SG). Others have used different terms over the years (e.g., AI for Social Impact or Data Science for Social Good) to represent various collections of studies, techniques, and (application/deployment) agendas. Organizations in private and public sectors (e.g., academic conferences and tech companies) have created opportunities for AI4SG studies. These opportunities include workshops and special tracks in conferences (e.g., AAAI, IJCAI, and KDD) for AI4SG and project fundings from companies (e.g., Google and IBM).
In this course, we will examine selected studies that can be grouped into AI4SG research in the areas of agriculture, education, environmental sustainability, healthcare, social care, public safety, and transportation.
This is a seminar class, in which the students in the class meet regularly (inside and outside of the classrooms) to read papers, discuss research, and conduct research under the guidance of the instructor in a collaborative environment. The students are expected to present research papers, participate in discussions, and conduct class/research projects. Note that our seminar class is different from the typical lecture-style classes, where the instructor provides lectures and covers specific topics from specific areas (e.g., a class of ML or a class of AI). We expect the students to have some background in some CS areas such as AI/ML and basic mathematical maturity.
Selected Papers from the following list of topics:
AI for Agriculture
AI for Education
AI for Environmental Sustainability
AI for Healthcare
AI for Social Care
AI for Public Safety
AI for Transportation