JOU 3365 Fall 2024

AI in Media and Society

“This is the first time I feel like I have really understood AI and I'm so grateful for that.”
— Student comment on course evaluation, fall 2022
“I understand so much more about how AI works and feel like I can hold my own in a conversation about it. Reading through academic journals for the research paper, I was able to read through their whole methodology and results and understand the technology they were using.”
— Student comment on course evaluation, spring 2022
“I think the lectures and required readings were incredibly useful for understanding these topics. Leaving this course I definitely feel that I have a much stronger grasp on how AI works and what its strengths and weaknesses are.”
— Student comment on course evaluation, spring 2022

THIS IS AN UNDERGRADUATE COURSE. Graduate students may not enroll in this course without permission from the instructor.

THIS IS NOT A SKILLS COURSE. Code and programming will NOT be part of this course.

PREREQUISITES: None. Students from any major may enroll in this course. No special background is required for this course.

This course meets 100 percent ONLINE. The two-period lecture discussion meeting is synchronous, and attendance in Zoom is required for the full two periods on one day only.

Required books:

Reading the book every week is necessary for your success in this course.

Description: Gain an understanding of artificial intelligence and machine learning as they apply to the media professions, including journalists reporting on AI. Explore major developments in AI technologies as covered by the mass media. Learn to detect hype and exaggeration in descriptions of AI’s promises and potential risks and dangers. Examine use of AI systems in finance, healthcare, hiring decisions, housing, policing, etc.

Students who complete this course will be able to:

  1. Evaluate news reports and corporate claims about AI systems, noting when claims are poorly supported or likely to be exaggerated.
  2. Explain how biases come to be “baked into” various AI systems, consequences of AI biases, and how biases could be reduced or eliminated.
  3. Describe uses of AI systems in finance, healthcare, hiring decisions, housing, policing and other domains, based on news reports.
  4. List limitations of trained AI systems used for image recognition and question answering, among other applications.
  5. Define and describe fundamental structures related to AI, such as algorithms, models, neural networks.
  6. Differentiate between machine learning and other types of AI.
  7. Summarize how present-day artificial intelligence differs from science fiction literature and films.
  8. Define and describe common concepts related to AI, such as “AI Spring,” “weak AI,” and “artificial general intelligence.”
  9. Explain the uses of some well-known datasets used in machine learning such as MNIST and ImageNet.
  10. Describe generally the operations and structure of deep neural networks for tasks involving images or language.

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