The project can be either theoretical or practical.
For the former, a solid background in mathematics is necessary,
as the student will attempt to develop and prove fairness
properties of algorithms. For the latter, the student should be
able to envisage an appropriate case study, such as mortgage
decisions or university admissions, collect data for it.
Mathematical maturity is still necessary, but the emphasis is
more on research methodology and programming skills. For this
particular project, our emphasis is on the sequential aspects of
fairness, more specifically fairness in Markov decision
processes. This includes the tasks:
- Formalize the decision problem.
- Specify the fairness criteria used.
- Develop an algorithm solving the problem while satisfying the fairness criteria.
Skills. The student should have good working knowledge of
calculus, probability and algorithms.
Benefits. The student will obtain background in
statistics, machine learning and constrained optimisation, as
well the links between these areas, as well as practical
experience in programming (mainly python/octave with some
performance critical functions in C) and/or optimality proofs.
Good theoretical or experimental results will lead to writing and
submitting a paper to a suitable peer-reviewed