Ethics of AI

Course content

This course introduces students to artificial intelligence applications, with particular attention to healthcare systems. After a technical overview of how some of these systems work from a programming and learning perspective, the course will offer students an overview of the various ethical, social, legal and political issues around AI and especially machine learning algorithms within healthcare and health informatics – but also their wider use within medicine and public health, and society at large. In particular, students will have the opportunity to learn about and work with ethical frameworks as lenses through which to analyze the implications of AI.

 

Students will analyse and discuss different systems and case studies in which AI is deployed, for example decision-support systems within oncology, pathology, and radiology. Some of the most important ethical issues around AI will be presented and discussed in class. For example: algorithmic bias, explainable AI, and data security and privacy issues relating to the large amounts of data used to develop machine learning models – and more in general the way in which AI is changing our healthcare and wider social systems; think, for example, of mental health applications or GPT chatbot technology.

Education

MSc in Medicine - elective course

The course will also be open to PhD students. PhD students should apply as external students in order to obtain a slot in the course:

https:/​/​healthsciences.ku.dk/​education/​student-mobility/​guest-students/​

Learning outcome

After completing the course the student is expected to:

 

Knowledge

  • Describe different forms and practices around artificial intelligence
  • Describe the difference between machine learning algorithms and traditional algorithms
  • Demonstrate knowledge of ethical frameworks relevant for AI-related contexts
  • Understand the impact of artificial intelligence on society and healthcare in particular, with special reference to issues of equality, bias and minorities
  • Describe the impact of specific issues relating to AI, including political, ethical and research issues.
  • Understand finer distinctions within this debate such as for example the ones between basic machine learning and deep learning, and between supervised vs. semi-supervised vs. unsupervised machine learning,

Skills

  • Critical analysis of the most important ethical and philosophical arguments relating to artificial intelligence, machine learning and tech systems more generally
  • Critical analysis and assessment of how theoretical concepts, principles and theories within ethics and philosophy apply to real life cases
  • Assess diverse healthcare systems and their different approaches to artificial intelligence, for example the difference between EU, UK and US (GDPR)

Competencies

  • Translate theoretical knowledge and principles on the ethics of artificial intelligence into social and political solutions
  • Communicate the relevance of artificial intelligence in health informatics contexts and also wider social and political domains

Class work will include student presentations, group work and plenum discussion of readings, with short lectures by the course leader.

Literature will be provided in due time through the usual channels: it is a mix of background readings in ethics and new research articles on artificial intelligence and machine learning within healthcare. 

Oral
Individual
Collective
Feedback by final exam (In addition to the grade)
ECTS
5 ECTS
Type of assessment
Oral examination, 30 minutes
Type of assessment details
Each student will do an oral exam of no more than 30 minutes (including grading and feedback) directly after the end of the course. In order to pass, at the exam, students will have the opportunity to briefly present the project they have worked on during the course, and then students will have to answer two or three questions about course material and readings.
Aid
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
Two internal examiners
Criteria for exam assessment

In order to obtain the grade 12, the student should be able to meet the learning objectives:

Knowledge

  • Describe different forms and practices around artificial intelligence
  • Describe the difference between machine learning algorithms and traditional algorithms
  • Demonstrate knowledge of ethical frameworks relevant for AI-related contexts
  • Understand the impact of artificial intelligence on society and healthcare in particular, with special reference to issues of equality, bias and minorities
  • Describe the impact of specific issues relating to AI, including political, ethical and research issues.
  • Understand finer distinctions within this debate such as for example the ones between basic machine learning and deep learning, and between supervised vs. semi-supervised vs. unsupervised machine learning,

Skills

  • Critical analysis of the most important ethical and philosophical arguments relating to artificial intelligence, machine learning and tech systems more generally
  • Critical analysis and assessment of how theoretical concepts, principles and theories within ethics and philosophy apply to real life cases
  • Assess diverse healthcare systems and their different approaches to artificial intelligence, for example the difference between EU, UK and US (GDPR)

Competencies

  • Translate theoretical knowledge and principles on the ethics of artificial intelligence into social and political solutions
  • Communicate the relevance of artificial intelligence in health informatics contexts and also wider social and political domains

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 20
  • Preparation
  • 117
  • Exam
  • 0,5
  • English
  • 137,5

Kursusinformation

Language
English
Course number
SMEA24001U
ECTS
5 ECTS
Programme level
Full Degree Master
Duration

1 semester

Placement
Autumn And Spring
Schedulegroup
See Syllabus
Capacity
50
Studyboard
The Study Board for Medicine and Health Science
Contracting department
  • Department of Public Health
Contracting faculty
  • Faculty of Health and Medical Sciences
Course Coordinator
  • Ezio Di Nucci   (4-677c6b714275777066306d7730666d)
    chief responsibility
Saved on the 05-04-2024

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