CS886 (Topics in AI) - Trust, Explanation, Social Networks 
Spring 2026 - Time: Friday mornings 830-1120 AM 

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.

During some classes, we will try providing opportunities to learn how
to do virtual presentations and how to participate well in these settings. 
Students will be asked to provide feedback on class presentations, commenting
as well on whether these talks were successful in a virtual setting.
This will be part of their assessment for class participation.

The class size will be restricted to 24.

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, done alone), relevant to course 
e) a group-oriented in-class exercise (with an individual component)

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 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 Aug 5 at 12noon; no extensions)

This is a tentative outline for the course

May 15 Introduction to course, Introduction to AI, XAI Sneak Peak
May 22  Intro to Trust Modeling and Explanation; Guest Speaker on LLMs
May 29 First Presentations (8)
Jun 5 First Presentations (8)
June 12 First Presentations (8)
June 19 Intro to social networking with guest speakers; Practice group exercise 
June 26 Second Presentations (8)
Jul 3 Second Presentations (8)
July 10 Second Presentations (8)
July 17 In-Class Group Exercise
July 24 Project Presentations (10); course summary and course evaluations
July 31 Project Presentations (14); Wrap up of course
Aug 5 (NO CLASS) Final projects due 12noon

Note1: Classes typically include a 10 minute break
Note2: First,Second Presentations include a 5 min QA transition between talks
Note3: Do NOT email Instructor paper preferences before HOW to explained May 15