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Practical Ethics for AI and ML
Provider: Faculty of Science

Activity no.: 5199-25-09-31There are 25 available seats 
Enrollment deadline: 11/04/2025
Date and time17.06.2025, at: 09:00 - 20.06.2025, at: 16:00
Regular seats25
Course fee2,400.00 kr.
ECTS credits2.00
Contact personIrina Alex Shklovski    E-mail address: ias@di.ku.dk
Enrolment Handling/Course OrganiserIrina Alex Shklovski    E-mail address: ias@di.ku.dk
Written languageEnglish
Teaching languageEnglish
Exam formCompleted/not completed
Exam detailsPre-assignment: students will be asked to select one paper from assigned literature prior to the course and prepare a short presentation to be delivered during the course. Participation in in-class activities, group-work and hands-on exercises Final paper reflecting on potential ethical challenges in own research
Course workload
Course workload categoryHours
Preparation12.00
Lectures10.00
Class Instruction8.00
Practical exercises12.00
Theory exercises5.00
Field Work8.00
Sum55.00
Content
Research area> AI, technology ethics, responsible computing, machine learning, NLP, critical computing.

The ethical debates around AI has already affected how machine learning and natural language processing are seen and what is expected from researchers in these domains. For example, the NeurIPS conference paper checklist now requires that papers discuss negative societal impacts of the submitted work where appropriate. Similar practices are emerging across many IEEE, AAAI and ACM conferences. How should we reason about sociatal impact of our work? What is broad enough? Which impacts are important to consider?
This course equips students with knowledge of ethical concerns and the principles behind these. We will present the relevant European regulations, and review existing techniques in responsible computing. Students will apply responsible computing techniques to their own research and explore how these address ethical concerns. We will also discuss the implications of existing responsible computing approaches and consider opportunities for research contributions in this area.

Aim and content
This course equips students with knowledge of ethical concerns and the principles behind these. We will present the relevant European regulations and review existing techniques in responsible computing. Students will apply responsible computing techniques to their own research and explore how these address ethical concerns. We will also discuss the implications of existing responsible computing approaches and consider opportunities for research contributions in this area.

Formal requirements

Formal requirements for applying for the course:

1) choose the "apply" button on the upper right side of the course description.

2) submit the following to course responsible Irina Shlovski, mail las@di.ku.dk
- A motivational letter that includes a description of research direction and goals (max. 10 lines)
- CV including info of any existing or in-progress publications as the course will engage with ongoing research through practical applications

Learning outcome
Knowledge:
• Current ethical concerns, principles, guidelines for AI
• Existing discussion in AI ethics, responsible computing, and AI-related European  regulatory frameworks.
• Theories and methods of responsible computing

Skills:
• Apply responsible computing techniques to research problems
• Analyze, discuss, and communicate potential benefits and pitfalls of responsible computing methods in relation to rights and ethics in society
• Evaluate technical problems for potential ethical challenges

Competences:
• Reflecting on ethical issues and societal consequences of the AI system implementation
• Examining the implications of technological solutions for societal challenges
• Identifying the relevant responsible computing approaches based on potential ethical issues and societal consequences

Literature
Course literature will be made available to students prior to the course (after enrollment)

Target group

>PhD students who engage with applications of AI and ML as well as students who are working on critical evaluations of AI and ML applications.

>MSc students aiming for preparation to enroll in PhD programs

 

Teaching and learning methods
Learning activities will include:
> Lectures
> Hands-on workshops with problem-based learning,
> Empirical data collection and analysis (field work)
> Flipped classroom
> Peer-to-peer (feedback sessions),
> Supported self-learning (preparation work and e-learning).

Lecturers
Ville Vakkuri, University of Vaasa, Finland
Niels van Dijk, Vrije University Brussels, Belgium

Remarks

Course location: TBD. 

Course fee:  All participants must pay a course fee DKK 2400 which covers location, course related educational activities and social activities.

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