20849 - ARTIFICIAL INTELLIGENCE, ALGORITHMS, AND ORGANIZATIONAL BEHAVIOR
Department of Management and Technology
Course taught in English
Go to class group/s: 31
CLMG (6 credits - II sem. - OP | SECS-P/10) - M (6 credits - II sem. - OP | SECS-P/10) - IM (6 credits - II sem. - OP | SECS-P/10) - MM (6 credits - II sem. - OP | SECS-P/10) - AFC (6 credits - II sem. - OP | SECS-P/10) - CLELI (6 credits - II sem. - OP | SECS-P/10) - ACME (6 credits - II sem. - OP | SECS-P/10) - DES-ESS (6 credits - II sem. - OP | SECS-P/10) - EMIT (6 credits - II sem. - OP | SECS-P/10) - GIO (6 credits - II sem. - OP | SECS-P/10) - DSBA (6 credits - II sem. - OP | SECS-P/10) - PPA (6 credits - II sem. - OP | SECS-P/10) - FIN (6 credits - II sem. - OP | SECS-P/10)
Course Director:
HEATHER YANG
HEATHER YANG
Mission & Content Summary
MISSION
Algorithms, and the artificially intelligent applications that are enabled by them, impact every aspect of the modern organization. The aim of the course is to allow students to apply principles of psychology and organizational behaviour onto the new frontier of the modern-day workplace. The course explores the main principles of organizational behaviour as applied to and as affected by the emergent technologies enabled by algorithms and artificial intelligence, as well as an introduction to practical tools and exercises to evaluate their use within organizations.
CONTENT SUMMARY
Students are first exposed to principles of psychology that lead to resistance to these technologies and then discuss data-driven strategies to improve their adoption. Then, students develop tools to critically evaluate current applications of algorithmic technologies in organizational settings, considering issues of efficiency, accuracy, firm performance, hiring and promotion decisions, equity, and the broader Future of Work.
The learning process is supported by guest lectures from organizations facing challenges caused by or solved by algorithmic technology, which will provide opportunities for students to apply the principles learned in class and propose solutions.
No background in either computer science or psychology is necessary.
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
The students should be able to identify principles of organizational behavior in novel technologies, critically evaluate current applications of algorithmic technologies in organizational settings, and discuss data-driven strategies to improve adoption of novel technologies.
- Teamwork
- Communication skills
- Presentation skills
- Intelligent question asking
- Critical thinking and analysis
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
In order to achieve individual learning objectives, the course will involve active participation from all of the students in a variety of modalities. By the end of the course, students should be able to identify principles of organizational behavior present in the roll-out of novel technologies, critically evaluate current applications of algorithmic technologies in organizational settings, discuss data-driven strategies to improve adoption of novel technologies, organize successful soft-skills such as effective teamwork in the generation of analyses, effective communication skills between team members as well as with other individuals in the classroom, successful presentation skills such as pacing and content curation.
Teaching methods
- Face-to-face lectures
- Online lectures
- Guest speaker's talks (in class or in distance)
- Exercises (exercises, database, software etc.)
- Case studies /Incidents (traditional, online)
- Individual assignments
- Group assignments
- Interactive class activities (role playing, business game, simulation, online forum, instant polls)
DETAILS
Guest speakers, either leading industry experts and/or academic experts, will give talks about specific course areas of technology or phenomena which will allow students to expand their understanding of phenomena as applied in real world contexts. Exercises will complement this broadening of contextual knowledge by giving students opportunities to put into practice theory learned in class onto hypothetical situations. Case studies will help elaborate theoretical concepts and give students practice in analytical thinking and perspective taking. Individual assignments will ensure that each student can independently think critically about the concepts covered in class, whereas group assignments will ensure that students are able to collaborate and understand how to be effective when working in a team. Interactive class activities will allow this development to occur while under the supervision of the instructor and will help students develop both content area expertise and as well as soft-skills necessary for the workplace.
Assessment methods
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ATTENDING STUDENTS
to be defined
NOT ATTENDING STUDENTS
to be defined
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
Attending students can use course slides as their main reference for exam preparation.
Non-attending students may request a specified reading list to guide their research literature reviews from the instructor.
Last change 22/07/2022 17:47