Study location	Netherlands, Maastricht
Type	Summer Course, full-time
Nominal duration	1 week (2 ECTS)
Study language	English

Tuition fee	€398 per programme

Entry qualification	Enrolled as an Undergraduate student or Undergraduate diploma The entry qualification documents are accepted in the following languages: English. Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original.

Language requirements	English

Overview

Coordinator:
Michael Thompson

Description:
In the last few years, we’ve seen a proliferation of products and services relying on Artificial Intelligence to personalize, automate and sometimes replace human cognitive activities. And yet too many of these new solutions have led to dramatic market failures – leading to monetary and/or reputational harms. In this course, we will look at different categories of AI project failures, beyond the technology, to better understand what went wrong. This non-technical course is aimed at future data scientists who want to take a cross-disciplinary approach to building their AI solutions that enables them to consider their projects from multiple perspectives. But as AI will have an impact across many business and societal activities, this course also targets those interested in entrepreneurial, business and management programs.

Course Duration and Dates
This is a one week course running from the 29th of June until the 3rd of July, 2020

ECTS
The number of credits earned after successfully concluding this course is the equivalent of 2 ECTS according to Maastricht University’s guidelines. For further information see the MSS terms and conditions

Goals
- Identify the different categories of AI project errors that might lead to reputational, financial or societal harm
- Introduce design processes, skills and profiles that must be considered as critical ingredients to any AI solution
- Explore practical cases where students can explore approaches to solving difficult challenges with AI projects

Instruction language
EN

Prerequisites
No prerequisites. While technology is an important component, this is not a technology course. Instead, students need only bring an exposure to and interest in how AI is rapidly reshaping our society. While this course if primarily relevant for students from DKE and Digital Society, it is also relevant for any program touching upon the implications of AI solutions – Economics, Law, Public Health, Health Sciences, International Business, Medicine, etc. Any student with a background in customer research, product design or service design will also find the content extremely relevant. There is also a strong ethics component that should resonate with a diversity of students’ programs. In fact, the more diverse the students’ programs of studies, the more relevant and useful the group work and practical exercises.

Recommend literature
Book: O’Neil, C. (2016) Weapons of math destruction : how big data increases inequality and threatens democracy. First edn. New York: Crown. [CHAPTER 4]
Video: “Nosedive.” Black Mirror. Netflix. Season 3. Episode 1.
Article: www.core77.com/posts/17993/Why-Design-Education-Must-Change?utm_source=core77&utm_medium=from_title
Article: André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Douglas, F., Goldstein, W., . . . Yang, H. (2018). Consumer choice and autonomy in the age of artificial intelligence and big data. Customer Needs and Solutions, 5(1-2), 28-37. doi:http://dx.doi.org.ezxy.ie.edu/10.1007/s40547-017-0085-8
Article: www.technologyreview.com/s/613578/ai-algorithms-liability-human-blame/
www.bloomberg.com/news/articles/2018-12-12/artificial-intelligence-has-some-explaining-to-do
Article: interactions.acm.org/archive/view/november-december-2018/design-and-fiction#R3
Article : medium.com/mit-technology-review/how-we-might-protect-ourselves-from-malicious-ai-5b7a895b75b3
Article: interactions.acm.org/archive/view/july-august-2017/intelligence-on-tap
Articles: design.google/library/ai/

Teaching methods
I will use cases derived from real-life AI solutions to illustrate cases for the students to work on. Each class will involve theory (as lecture) but also hands-on group work.

Assessment methods
Attendance
Presentation
Take home exam