Course 2023-2024 a.y.

20904 - MANAGING ARTIFICIALLY INTELLIGENT DIGITAL PRODUCTS

Department of Management and Technology

Course taught in English
Go to class group/s: 31
CLMG (6 credits - I sem. - OP  |  SECS-P/08) - M (6 credits - I sem. - OP  |  SECS-P/08) - IM (6 credits - I sem. - OP  |  SECS-P/08) - MM (6 credits - I sem. - OP  |  SECS-P/08) - AFC (6 credits - I sem. - OP  |  SECS-P/08) - CLELI (6 credits - I sem. - OP  |  SECS-P/08) - ACME (6 credits - I sem. - OP  |  SECS-P/08) - DES-ESS (6 credits - I sem. - OP  |  SECS-P/08) - EMIT (6 credits - I sem. - OP  |  SECS-P/08) - GIO (6 credits - I sem. - OP  |  SECS-P/08) - DSBA (6 credits - I sem. - OP  |  SECS-P/08) - PPA (6 credits - I sem. - OP  |  SECS-P/08) - FIN (6 credits - I sem. - OP  |  SECS-P/08)
Course Director:
COLIN PRESCOTT MacARTHUR

Classes: 31 (I sem.)
Instructors:
Class 31: COLIN PRESCOTT MacARTHUR


Mission & Content Summary

MISSION

ChatGPT, DALL-E and other meteoric, AI-based products show that algorithm-based technologies are increasingly important to many organizations. But how can companies incorporate these technologies to seize market opportunities, but limit their technical, legal and ethical downsides? This course’s focus is on learning the NON-TECHNICAL skills necessary to design and manage digital products that incorporate artificial intelligence successfully. It draws from many disciplines, including technology management, human factors psychology, sociology of computing, and others. Speakers from companies and public institutions using artificial intelligence will offer example challenges and solutions.

CONTENT SUMMARY

  • What is “artifical intelligence” and how has it evolved?
  • The basics of product management and design, in a pre-AI world
  • The opportunities and challenges of incorporating AI in digital products, including: 
    • Business model shifts
    • Data droughts
    • Algorithmic aversion and appreciation
    • Privacy laws, including GDPR
    • Limits of human supervision
    • Inadvertent effects + ethical dilemmas
  • Design solutions to common problems, including:
    • Ethnographic observation
    • Prototyping and outcome measurement
    • Guidelines and frameworks
    • Transparency tactics and design patterns
    • Auditing
  • Case studies from higher education, healthcare, “big tech” and government

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Learn the basic terminology of artifical intelligence, and use it in a correct way
  • Name opportunities and challenges that many AI-based organizations face

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Plan and conduct ethnographic observations AI-based products
  • Develop prototypes of changes to AI-baed products, and measure their outcomes
  • Evaluate guidelines and frameworks for ethical AI
  • Use auditing techniques for AI products

Teaching methods

  • Face-to-face lectures
  • Guest speaker's talks (in class or in distance)
  • Exercises (exercises, database, software etc.)
  • Case studies /Incidents (traditional, online)
  • Group assignments
  • Interactive class activities on campus/online (role playing, business game, simulation, online forum, instant polls)

DETAILS

Students will learn basic terminology and challenges through in-class lectures, which will include guest speakers, polls, classroom discussion and other activities. These interactive lectures will allow students to quickly build a deep understanding of the relevant issues. Again, course sessions are highly interactive. Do not expect to come get lectured at; come ready to participate in your own learning.

 

Students will apply much of what they learn in a group project. During this project, students will chose an AI-based product to work on (either one that already exists, or one they would like to create). They will then apply varying tools from our lessons to their product, and share their results with me in a written report.


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
     x
  • Group assignment (report, exercise, presentation, project work etc.)
     x

ATTENDING STUDENTS

Consistent with the learning outcomes, grades will be based on:

  • A group project (65% of the final grade) to assess the student’s ability to design digital products that overcome challenges of algorithmic business. See description above. The group project will be due the last day of class.
  • An individual final exam (35% of the final grade) to assess the student’s understanding of the basic terminology of the AI-driven business world, and the challenges and solutions they encounter. Most of the exam will be multiple-choice. There will be a single, open-ended question.

NOT ATTENDING STUDENTS

Consistent with the learning outcomes, grades will be based on:

  • A large, individual project (65% of the final grade) to assess the student’s ability to design digital products that overcome challenges of algorithmic business. See description above. This project will be the same as the attending students' group project, but will be completed alone, instead of in a group. The individual project will be due the last day of class.
  • An individual final exam (35% of the final grade) to assess the student’s understanding of the basic terminology of the AI-driven business world, and the challenges and solutions they encounter. Most of the exam will be multiple-choice. There will be a single, open-ended question.

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

Selected readings, slides, cases, and exercises will be made available on Blackboard. 

 

Last change 05/06/2023 21:51