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Lehrende: Dr. Thi Thanh Huyen Nguyen
Veranstaltungsart:
Seminar
Anzeige im Stundenplan:
Semesterwochenstunden:
2
Credits:
6,0
Unterrichtssprache:
Englisch
Min. | Max. Teilnehmerzahl:
- | 1
Anmeldegruppe: Profilbildungsseminar
Kommentare/ Inhalte:
A basic understanding of statistics and probability theories is required.
All students are required to check if they understand the basics of calculus, linear algebra, and probability, in part I from this online book.
Lernziel:
The world we are living in is increasingly automated, from basic clerking and cashier activities at the supermarkets to recruitment, doctor diagnosis and court decisions. For certain tasks, Artificial Intelligence (AI) systems have achieved good enough performance to be deployed in our streets and our homes, such as object and speech recognition (e.g. Siri, Alexa). For other tasks, AI systems have even exceeded human performance. The prime example is AlphaGo, which is the first computer program to beat the best Go player in the world.
Despite the vast potential of AI development to transformatively improve our lives, recent research has demonstrated shortcomings of AI, particularly from the egalitarian standpoint. Risk assessment tools used in US courts have shown to be biased against black people; whereas corporate recruiting algorithms in certain major companies, for instance, have been shown to be biased against women.
This course is a series of seminars discussing the latest research into the integration of AI into various domains, along with possible algorithmic biases and their implications in society. The course begins with an overview of the state of automation across various parts of life, along with how such automation trends could impact implicit biases and discrimination attitude across social groups. Weekly readings and presentations include state-of-the-art academic articles and documentaries on the applications of AI in health, courts, hiring and credit rating applications. By engaging in a series of peer presentations and final research project activities, students can equip themselves with a comprehensive understanding of the good, the bad and the not-so-pretty of AI applications in our societies.
Vorgehen:
The course is organized as an interactive, in-person class sessions. 4 sessions are lecture/project consultation only, whereas the other 10 sessions will include short lectures by the instructor, followed by: (1) presentations of course readings by students, in groups of two and (2) “critique”/discussant presentations by two other students. Non-presenting students in any particular week should read these materials in advance and pay attention to their classmates, to engage and ask questions, as part of their participation assessment.
This format allows plenty of time to ask questions during lecture hours. In addition, office hours are provided on demand. The exact format and logistics of the course is provided in class.
Since in-class presentations and discussions are the fundamental learning block of this course, please do NOT register for the course if you intend to do it online.
Literatur:
The course will be based on the weekly lecture slides and provided reading materials. These materials will be available on Open Olat upon the commencement of the course.
The following books can be used as reference along with the slide content.
Weapons of Math Destruction – Cathey O’Neil (2017)
Big Data: A revolution that will transform how we live, work and think – Viktor Mayer Schonberger, Kenneth Cukier (2014)
Numbersense: How to Use Big Data to Your Advantage - Kaiser Fung (2013)
Bit by bit: Social Research in the Digital Age – Matthew Salganik (2017)
Zusätzliche Hinweise zu Prüfungen:
There will be no written centralized exam. Course assessment includes the following components:
20%: Paper presentation
10%: Critique/Discussant presentation of other’s presentation
70%: Final research project oral examination
In addition, active participation in weekly class sessions will yield a maximum grade bonus of 0.7.
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