Human-Centred Machine Learning

Course manual 2025/2026

Course content

The ultimate goal of machine learning (ML) and AI is often considered to be that of building fully autonomous systems. However, every stage of building these systems involves humans, from the data, to design, and deployment. Understanding this interaction with humans, as well as the influence (both intentionally and unintentionally) that humans may have on AI systems and AI systems on humans is at the core of this course. Questions surrounding how to design and build AI systems which prioritise and adapt to human needs and preferences, as well as considerations on whose values are being encoded and what trade-offs are being made will be discussed.

This course is centered around the role of humans in all stages of ML and AI systems, including discussion of techniques for integrating human intelligence and methods for designing systems centered on humans. Topics addressed in the course include: ethics in AI, crowdsourcing, machine teaching, learning from human feedback, and human-centered evaluation.

Study materials

Literature

• Reading list of academic articles will be provided.

Objectives

• The student can state key concepts in human-centred data work (e.g. evaluation, annotation, crowdsourcing)
• The student can list techniques for building systems that adapt to human feedback ((inter)active learning, RLHF, machine teaching)
• The student can analyse a given intelligent system to identify sub-components that can be improved for humans
• The student can evaluate the performance of a hybrid human-AI system and identify sub-components in need of improvement
• The student can create a human-centred AI system that effectively solves a problem of practical consequence

Teaching methods

• Lecture
• Presentation/symposium
• Self-study
• Working independently on e.g. a project or thesis
• Seminar

Attendance

This programme does not have requirements concerning attendance (OER part B).

Assessment

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Additional information

Additional course material will be made available at: https://amsterdam-hcml.github.io/

Contact information

Coordinator

• Nanne van Noord