Details

Full details will be available in syllabus here.

Bulletin Description

This will be an advanced Artificial Intelligence (AI) course focused on understanding reasons why Artificial Intelligence (AI) systems can be problematic and what can be done to make them trust-worthy. We will briefly cover AI as a decision support technology involving data processing, generating analysis and communicating insights to users. Then we will study how output of AI can be sensitive to issues in data, algorithmic steps (pre-, during and post-) and interaction with people of diverse background. We will conclude with techniques to remove or mitigate issues. The students will be exposed to latest tools and will do a project using AI technique of their choosing. The course will cover AI sub-fields of learning, reasoning, representation, preferences and uncertainty.

Prerequisites

Experience with a first course in data structures (CSCE 350), programming (CSCE 330) and introductory AI course (CSCE 580, 590 or equivalent) is advised.

 Learning outcomes

As a result of successful participation in this course, undergraduate students will be able to:

L1: Explain, execute and create AI-based analytical methods to process data: (a) unstructured data, (b) semi-structured data, (c) structured data

L2: Explain AI methods in data analysis: (a) Learning methods, (b) Reasoning, (c) Representation and standardization – knowledge graphs/ ontology, (d) Preferences, (e) Handling Uncertainty

L3: Identify trust issues in AI methods: (a) fairness and bias, (b) harmful language, (c) data privacy

 L4: Methods and tools to promote trust: (a) Data sampling and synthetic data, (b) Testing and rating for communication, (c) Algorithmic innovations like differential privacy and explanations

As a result of successful participation in this course, graduate students will be able to do all of the above, and:

L5: Evaluate gaps in Trusted AI tools and create new datasets to handle them

 L6: Explain emerging standards, frameworks and laws.

 L7: Explain research findings in open areas and critique their contributions

Evaluate gaps in analytical methods and create new ones to process data