Want to Learn About Artificial Intelligence for the Real World? 

In recent years, Artificial Intelligence (AI) has generated wild excitement due its potential to transform businesses and societies around the world with technologies like ChatGPT, Dall-E, self-driving cars, Google Maps, Machine Translators, and Shazam, and yet, it has evoked tremendous fear and doomsday warnings about possible negative impacts like increasing unemployment, exacerbating injustices, data breaches and circumventing humans oversight. How does one learn AI to use them in the real world while also steering around practical issues? Enter the new course.

Trusted AI has become a regular course from Fall 2023 onwards. Full details are available with syllabus here. Latest location and timing information are on the left.  (It was earlier taught as a special topic course in Fall 2021. See schedule and slides here.)

Bulletin Description

Trusted AI is 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.  All along, we will take a practical approach to AI to balance the benefit/cost tradeoff. 

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

Expectations

• Understand trust issues and work with AI responsibly for hard, real-world, problems

• Schedule for Spring 2025 will be (> 30%) different from previous courses