Practical Research in Human-centred AI
Principal lecturer: Prof Alan Blackwell
Additional lecturer: Dr Advait Sarkar
Taken by: Part II CST
Code: HCAI
Term: Michaelmas
Hours: 16 (8 x 2-hour sessions)
Format: In-person lectures
Prerequisites: Further Human–Computer Interaction
Moodle, timetable
Aims
This is an advanced course in human-computer interaction, with a specialist focus on intelligent user interfaces and interaction with machine-learning and artificial intelligence technologies. The format will be largely Practical, with students carrying out a mini-project involving empirical research investigation. These studies will investigate human interaction with some kind of model-based system for planning, decision-making, automation etc. Possible study formats might include: System evaluation, Field observation, Hypothesis testing experiment, Design intervention or Corpus analysis, following set examples from recent research publications. Project work will be formally evaluated through a report and presentation.
Lectures
(note that Lectures 2-7 also include one hour class discussion
of practical work)
• Current research
themes in intelligent user interfaces
• Program
synthesis
• Mixed initiative
interaction
• Interpretability /
explainable AI
• Labelling as a
fundamental problem
• Machine learning
risks and bias
• Visualisation and
visual analytics
• Student research
presentations
Objectives
By the end of the course students should:
• be familiar with
current state of the art in intelligent interactive systems
• understand the human
factors that are most critical in the design of such systems
• be able to evaluate
evidence for and against the utility of novel systems
• have experience of
conducting user studies meeting the quality criteria of this
field
• be able to write up
and present user research in a professional manner
Class Size
This module can accommodate upto 20 Part II, Part III and MPhil students.
Recommended reading
Brad A. Myers and Richard McDaniel (2000). Demonstrational Interfaces: Sometimes You Need a Little Intelligence, Sometimes You Need a Lot.
Alan Blackwell (2024). Moral Codes: Designing alternatives to AI
Assessment - Part II Students
The format will be largely practical, with students carrying out an individual mini-project involving empirical research investigation.
Assignment 1: six incremental submissions which together contribute 20% to the final module mark.
Assignment 2: Final report - 80% of the final module mark