Insegnamento a.a. 2022-2023

20848 - DESIGNING PRODUCTS FOR ALGORITHMIC ORGANIZATIONS

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

Algorithms, artificial intelligence and machine learning are changing the way organizations do business. If you want to get a job in a large technology company, digital consulting, or governmental organization, how to grapple with organizations’ strengths and weaknesses. This course’s focus is designing digital products that seize opportunities in algorithmic organizations. You will learn about organizations that rely on repeating, computerized, data-driven processes. Then the course will introduce these entities’ practical challenges and common goals. You will practice architecting digital products that address these needs. Classes will draw from quantitative human-centred design, data science and other multi-disciplinary perspectives. The course aims to provide hands-on experience solving problems in a fast-growing sector. Speakers from algorithmic companies and public institutions will offer example challenges and solutions.

CONTENT SUMMARY

  • What is “algorithmic business” and how has it evolved?
  • The basics of product design, in a pre-AI world
  • The challenges of algorithmic organizations, including: 
    • Business model shifts
    • Data droughts
    • Algorithmic aversion and appreciation
    • Privacy laws, including GDPR
    • 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 the algorithmic business world

·       Build a deep understanding of the challenges that algorithmic businesses face

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...

·       Plan and conduct ethnographic observations of algorithmic products

·       Develop prototypes of modified algorithmic 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 (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.

 

As the course shifts to focusing on solutions, much of the class time will be devoted to working on a group project. For each group project, students will chose an algorithmic product to work on. During class meetings, the course instructor will demonstrate a design method, and then students will implement it on a case study of their own. These workshops will allow students to practice real-life skills necessary for contributing to an algorithmic business. 


Assessment methods

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

ATTENDING STUDENTS

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

  • Group project assignments (65% of the final grade) to assess the student’s ability to design digital products that overcome challenges of algorithmic business. These will be approximately 10 small assignments, which can be completed during the class meeting time if students are present.
  • An individual, multiple choice final exam (35% of the final grade) to assess the student’s understanding of the basic terminology of the algorithmic business world, and the challenges and solutions they encounter. 

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. This project will include approximately 10 components (which mirror the in-class group project assignments). Non-attending students are expected to complete the project to a similar level of quality as attending students. 
  • An individual, multiple choice final exam (35% of the final grade) to assess the student’s understanding of the basic terminology of the algorithmic business world, and the challenges and solutions they encounter. 

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

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

Last change 07/06/2022 21:12