ACIT4005 Technology and society: Critical perspectives in practice Course description

The rapidity and radicality of new and emergent technological developments have potentially far-reaching consequences that may lead to major changes in our civilization and confronts us - as individuals and societies - with new ways of knowing and acting, and pivotal questions, challenges, and opportunities. Addressing these issues requires a broad and interdisciplinary approach: it is increasingly important that future engineers, computer and data scientists, and technologists in general, can reason about the possible implications of technology in their own field as well as society at large.

Concrete questions concerning technology and technological development are examined through historical and contemporary texts, concrete and hypothetical technologies, and relevant real-world cases. Through lectures, colloquial presentations, discussions and writing, students will develop an advanced understanding of the interconnectivity of technology and society and familiarise themselves with theoretical and analytical tools and terms that enables discussions and critical assessments of technological trends, tasks, and practices. This course gives the students opportunities to explore and critically examine key dimensions of technologies and methods that will be part of their master’s thesis.

All aids are permitted, as long as the rules for source referencing are complied with.

None.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills, and general competence:

Knowledge

Upon successful completion of this course the student has an in-depth understanding of:

• the interconnectivity of technology and society
• central critical perspectives on technology
• the relevance of historical knowledge of technology
• different tools and methodologies which can be applied to understand and analyse the roles and effects of technology

Skills

Upon successful completion of this course the student can:

• evaluate and critically discuss the possibilities and challenges of technological solutions used in the profession(s) relevant to their field of study and present the arguments in a structured form.
• apply philosophical and analytical strategies to critically discuss the possibilities and challenges of existing and potential technologies.
• formulate and present advanced and informed arguments on complex and intractable problems that arise in the development and implementation of technology.
• position his/her research focus and professional interests in a broader context.

General Competence

Upon successful completion of this course the student can:

• communicate critical concepts, theories and perspectives related to technology in a structured manner.
• assess a critical framework of their own practice in their future work.
• discuss ethical, social, and political challenges arising at the intersection of technology and society.
• examine contemporary, real-world cases from multiple perspectives.
• evaluate the potential impact of modern technologies and digitalization processes on an individual and societal level.

The classes consist of lectures and seminars. Students are expected to participate in discussions, to give short presentations on assigned topics/texts. There will be text workshops with group supervision.

In this course, students will acquire an understanding of some of the most important principles of data science and cognitive technologies through project work and online resources. The students will be introduced to fundamental principles of machine learning, data science and artificial intelligence. The main focus will be on how to use these principles to solve industrial tasks by using open-source or other data science platforms. The goal is to provide the students with an introduction to machine learning, data science and artificial intelligence using online resources at the same time as the students solve an industrial problem in the form of a comprehensive project work.

In addition to the projects on offer, students can find their own projects within a relevant company, public organization or nonprofit. In this case, it is the student's responsibility to find a supervisor for the project within the external organization. All student-initiated projects must be approved by a supervisor at OsloMet before the start of the project. 

The workload for the project should correspond to two days a week over a twelve-week period during either the Spring or Autumn semester. If the project is completed in the summer, the workload should equal four days a week over a six-week period.

The elective course will only run if a sufficient number of students a registered.

No requirements over and above the admission requirements.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and competence:

Knowledge

The student:

• has a basic technological understanding of the most important concepts in machine learning, data science and artificial intelligence
• has knowledge of the most important methods in machine learning, data science and artificial intelligence
• has knowledge of platforms that can be used to complete major data science projects (for instance IBM Watson’s cloud services)

Skills

The student:

• masters basic data science tools and can extract and visualise information from large quantities of data
• understands the workflow in bigger data science, artificial intelligence or machine learning projects
• is capable of using open-source and commercial tools that are used in industrial projects in the fields of data science, machine learning or artificial intelligence

General competence

The student:

• masters methods and tools used to develop and carry out projects in data science, machine learning or artificial intelligence
• is familiar with the different methods that are used to find the right tools to carry out data science projects
• has an overview of how to visualise and manipulate data and how to develop predictive methods for solving industry problems and other issues relevant to working life

Grade scale A-F.

One internal examiner will be used. External examiners are used regularly.

Written project report (100% of the final grade).

A written project report delivered at the of the semester, individual or in groups (max 5 students), 3000 words +/-10 %.

For group projects, all members of the group receive the same grade. Under exceptional circumstances, individual grades can be assigned at the discretion of the project supervisor(s) and Head of Studies.

The exam result can be appealed.