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Department of Computer Science

The Effect of Uncertainty when Implementing Machine Ethics

Primary supervisor

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Other projects with the same supervisor


  • Competition Funded Project (Students Worldwide)

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

Machine Ethics is the study of how ethical reasoning can be computationally implemented. Approaches to Machine Ethics are divided into top-down and bottom-up approaches. In top-down approaches the machine reasons explicitly about ethics using utilities or symbolic rules while in bottom up approaches the machine learns ethical behaviour from observation. This project is primarily concerned with top-down approaches. Top down approaches to Machine Ethics generally draw upon theories of ethics from Philosophy such as utilitarianism or Deontic Logic. In general these philosophical theories deal with certainties, particularly when reasoning about the outcomes of actions - so they assume that some circumstance, such as a person being hurt, either will or will not be the outcome of the action. In some cases computer science has adapted these theories to incorporate uncertainty - for instance Markov Decision processes can be seen as a variation on utilitarianism that uses probabilities to calculated expected, rather than actual, utilities. However, in general, the effect of uncertainty on ethical reasoning has not been well-studied in computer science, and there is a lack of proposals for implementational frameworks that incorporate uncertainty into ethical reasoning.

This PhD would involve identifying key top-down approaches to the implementation of machine ethics and proposing mechanisms to adapt these approaches to account for uncertainty. Cases studies, preferably ones that could illuminate the difference between different implementational approaches, and between certainty and uncertainty would need to be developed in order to evaluate the proposals. Machine learning techniques could potentially be adopted in order to learn probabilities for the outcomes of actions that could then be incorporated into the ethical reasoning.

Minimum Viable PhD: The first year of the project would involve reading about top-down approaches to machine ethics and selecting at least two for further study. These could (though need not) be approaches already investigated by the primary supervisor and implemented within the Agent Infrastructure Layer that she has developed. In the first year the student would also consider the issue of uncertainty and the ways the selected approaches might be adapted to account for uncertainty. An initial proposal for a case study for evaluation of the project would be sketched out. Ideally this work would result in a position paper in one of a number of workshops devoted to machine ethics and ethics and AI. The second year would focus on an iterative process of implementing, evaluating and refining the proposed adaptations of the selected approaches to account for uncertainty. The third year would focus on writing up the results of the second year, both as papers in significant conferences and as part of the thesis. Implementations would be made publicly available (e.g., on GitHub).

Person specification

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Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.


Applicants will be required to evidence the following skills and qualifications.

  • You will have good time management.
  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.


Applicants will be required to address the following.

  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Why do you believe you are suitable for doing Postgraduate Research?