CS886 (Topics in AI) - Trust, Explanation, Social Networks - Prof. Robin Cohen
Spring 2024 - Time: hoping for Fridays 2.5 hour morning slot (Online)
** New as of Apr 1, 2024: course is scheduled 9am-1150am Fridays (online)

In this course, we will examine the context of online social networks
from the perspective of artificial intelligence, including an exploration
of the subfield of trust modeling in multiagent systems and its
application to social networking environments.
We will also examine how to achieve AI explainability, towards improved trust.

Background in artificial intelligence is beneficial but not essential.
In an early lecture, a brief introduction to AI will be provided.

Students will be exposed to a variety of current research in a
very topical area of study. They will have the opportunity to gain
skill in oral communication (through detailed feedback provided on
their class presentations) and in concisely conveying detailed academic work.
Students will also learn important analytic skills, executed in a limited
timeframe, through the group exercise.

This course will be delivered entirely online. A value-added of the course
will be providing students with opportunities to learn how to do virtual
presentations and how to participate well as audience members of these talks.
Students will be asked to provide feedback on class presentations, commenting
as well on whether these talks were successful in the online setting.
This will be part of their assessment for class participation.

Audits are welcome. Ask the instructor for permission; audits will be
asked to attend a certain number of classes and to complete the feedback
sheets on the students doing presentations that day.

The ideal class size is no more than 21.

Each student in the class will be required to complete:
a) a 15 minute oral presentation of a research paper from a predetermined list,
once for either the topic of Trust or Explanation and once for either the topic
of Explanation or Social Networks 
b) a one-page point-form summary of the above research paper to be
distributed as a handout to the class, for the first of the presentations in a)
c) a 10 minute oral presentation of the student's final research project
d) a final research project (student's choice), relevant to the course topic
e) a group-oriented in-class exercise (with an individual component)
f) a class-participation exercise providing commentary on presentations of peers

Note that the paper presentations will be delivered in real-time, in class
while the project presentation will be pre-recorded and played in class.
This will provide students with practice in two types of online talks.

The final grade will be computed as follows:

Each paper presentation 10 marks each (20 marks)
One-page summary of FIRST paper presented only (10 marks)
Class Participation Commentary 10 marks
Final project presentation 10 marks
Group exercise 10 marks
Final project 40 marks (due July 29 at 11am; no extensions)

This is a tentative outline for the course

May 10 Introduction to course, Introduction to AI, XAI Sneak Peak
May 17  Intro to Trust Modeling and Explanation; Guest Speaker on LLMs
May 24 First Presentations (7)
May 31 First Presentations (7)
June 7 First Presentations (7)
June 14 Intro to social networking with guest speakers; Practice group exercise 
June 21 Second Presentations (7)
June 28 Second Presentations (7)
July 5 Second Presentations (7)
July 12 In-Class Group Exercise
July 19 Project Presentations (14)
July 26 Project Presentations (7); Wrap up of course
July 29 (NO CLASS) Final projects due 11am