GRA 6811 Artificial Intelligence, Algorithms and Society

GRA 6811 Artificial Intelligence, Algorithms and Society

Course code: 
GRA 6811
Communication and Culture
Course coordinator: 
Eliane Bucher
Christian Fieseler
Course name in Norwegian: 
Artificial Intelligence, Algorithms and Society
Product category: 
MSc in Business - Elective course
2023 Autumn
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

Computers, Artificial Intelligence (AI) and Algorithms are dramatically changing the way we live, work and do business. The last years have seen unprecedented innovation in areas such as large-scale information processing, problem solving and machine learning. In various work contexts, computers can complete tasks not only – by an order of magnitude – faster than humans, they are also more efficient, more reliable, and they seem much more creative in devising adequate problem-solving strategies. AI and Algorithms are not only used to make sense of enormous amounts of existing data, but they can also be used to make predictions about the future – as such, they become a crucial tool for decision making. At the same time, AI and Algorithms have sparked heated and often contradictory discussions in the public sphere which encompass both dystopian and utopian narratives. Here, headlines range from rouge ‘killer robots’ or ‘artificial intelligences seeking world domination’ to solving ‘computers solving hunger, poverty and illiteracy’. Today’s decision-makers will not only have to use AI and Algorithms as a part of their daily work, but they also need to understand the public discourse surrounding these phenomena and be able to make educated and measured choices.

In this course, we will explore the foundational issues that comprises current developments in artificial intelligence, primarily form a philosophy of mind perspective. Based on a solid understanding of the interplay between minds and machines, we will the proceed conceptualizing future scenarios for business and society, and deliberate on the possible economic and ethical outcomes of these.

Learning outcomes - Knowledge

The goal of this course is to enable participants to take on an informed and measured stance vis-a-vis new technologies in general and AI and algorithms in particular.

  • What are the philosophical foundations of intelligence, consciousness and mind?
  • What are Computers, AI and Algorithms capable of – and where are their (current) limitations?
  • What are ethical implications of Computers, AI and Algorithms as either tools or agents in modern work and living environments?
  • Which trajectory will Computer, AI and Algorithmic development take? Which are the best-case and which are the worst-case scenarios?
  • How can we manage Computers, AI and Algorithms in the modern workplace?
Learning outcomes - Skills
  • The ability to distinguish the different elements that make up the current advancements in artificial intelligence, and to be able to discern future patterns and impacts.
  • Mapping probable future developments and outcomes based on scenario techniques
  • Discerning the uneven impacts of technology using stakeholder analyses
General Competence
  • Developing an overview of the interdisciplinary study of cognition, information, communication, and language, that underpins research in artificial intelligence, with an emphasis on foundational issues.
  • Understanding the foundational issues at the interplay between minds and machines: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent?
  • Reflection of the ethical and social implications that the application of artificial intelligence may bring to business and society.
Course content

Current and Future Use of Artificial Intelligence

Challenges resulting from and solved through AI in Business and Society

Scenario Techniques and How to Map Possible Futures


Guiding Questions

  • What is (artificial) intelligence?
  • What are algorithms?
  • A brief history of thinking machines
  • What are the unanswered questions in AI?

02_Bodies and Minds

Guiding Questions

  • Could calculating machines have pains?
  • How are mental states related to brain states and behavior?
  • What are the main questions in the philosophy of mind?
  • What distinguishes Dualists, Materialists and Functionalists?


Guiding Questions

  • What does the Chinese Room argument posit with respect to mind, understanding and consciousness?
  • How does the Chinese Room argument relate to Artificial Intelligence?
  • What are objections brought up against the Chinese Room argument?
  • What do zombies have to do with the Chinese Room Argument?

04_How computers work

Guiding Questions

  • How are algorithms used for systematic problem solving?
  • Why do different forms of representation matter for computing algorithms?
  • What constitutes a good algorithm?

05 & 06_Machine Learning & Deep Learning


  • How can machines learn?
  • What are algorithms?
  • What are the components of machine learning?

07_Economic Impact


  • In which ways will artificial intelligence have an impact on the economy?
  • Which kind of jobs will be impacted the most through artificial intelligence?
  • How to design an inclusive economy in an age of ubiquitous artificial intelligence?

08_Ownership and Access


  • Which impacts will artificial intelligence have on the competitiveness of markets?
  • Which disruptions in current business ecosystem for AI do you find conceivable?
  • Are there any foreseeable social implications given the likely trajectory of AI businesses?

09_Transparency and Accountability


  • What are the ethical issues surrounding big data and algorithms?
  • Which solutions are there to make AI’s fairer, and more inclusive?
  • Which areas of AI development need either better corporate social responsibility of governmental intervention, if any?

10_Autonomy and Agency


  • What are some of the challenges and harms of AI systems
  • What are the potential benefits of AI systems, and what are good tradeoffs versus potential harms?
  • How to effectively design and regulate AI systems for society’s benefit?

Future Scenarios: Poster Presentation and Discussion

Teaching and learning activities

The course combines formal lectures meant to together discuss the themes encountered in the readings and to help with any problems in understanding the foundational concepts, with exploration of how these will impact the business and social context. The course will consist of the following elements:

  • Formal lectures to gain a collective understanding of the readings;
  • guest lectures by practice experts to gain insights on artificial intelligence
Software tools
No specified computer-based tools are required.
Additional information

The exam for this course has been changed starting academic year 2023/2024. The course now has one exam code instead of three. It is not possible to retake the old version of the exam. Please note new exam codes in the Exam section of the course description. 

It is the student’s own responsibility to obtain any information provided in class.


All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.


Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Form of assessment:
Written submission
Exam code:
GRA 68115
Grading scale:
Grading rules:
Internal examiner
Examination when next scheduled course
100No1 Semester(s)Group/Individual (1 - 3)Final paper.
Exam category:Submission
Form of assessment:Written submission
Grouping (size):Group/Individual (1-3)
Duration:1 Semester(s)
Comment:Final paper.
Exam code:GRA 68115
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
Student workload
36 Hour(s)
Prepare for teaching
Group work / Assignments
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.