Building Interpretable AI Systems

Primary supervisor

Additional supervisors

  • Tingting Mu

Contact admissions office

Other projects with the same supervisor

Funding

  • 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

Despite the popularity and recent advances in Artificial Intelligence (AI) systems boosted by machine learning (ML) methods, most of the existing models fall short on their ability to explain their reasoning process, i.e., in providing human-like justifications for the reasoning behind a certain AI task. This functionality of "being interpretable" is a fundamental requirement for the adoption and uptake of AI systems in real-world scenarios, as users need to trust and understand the approximations and inferences done by the system.

The application of AI in contexts with high social and economic impact (as in health care and legal settings) will require the evolution of black-box AI models in the direction of systems which can justify, explain and dialogue with their end-users about the underlying reasoning process, providing transparent human-interpretable outputs.

This project aims at designing, building and evaluating a framework for the construction of Interpretable AI systems, with an emphasis on Natural Language Processing (NLP) tasks. The goal is to support the construction of complex AI systems for addressing tasks such as Question Answering and Text Entailment, which can output meaningful human-like explanations in addition to the expected output. Part of the project will involve the investigation of interpretable machine learning approaches for NLP, e.g., through exploratory analysis and visualisation of intermediate results returned by a ML model and improvement of model architecture design.

Applicants are expected to have:

* An excellent undergraduate degree in Computer Science or Mathematics (or related discipline), and preferably, a relevant M.Sc. degree.
* Confidence and independence in programming complex systems in Java or Python. Industry experience is a plus.
* Previous academic or industry experience in Natural Language Processing or Machine Learning (desired).
* Excellent report writing and presentation skills.

Please note that applicants must additionally satisfy the standard requirements for postgraduate studies at the University of Manchester, such as a first-class or high upper-second class (or an equivalent international qualification) and English language qualifications, as stated in the PGR guidelines.

Qualified applicants are strongly encouraged to informally contact Andre Freitas (andre.freitas@manchester.ac.uk) and Tingting Mu (tingting.mu@ manchester.ac.uk) to discuss the application prior to applying.

Funding Notes

Candidates who have been offered a place for PhD study in the School of Computer Science will be considered for funding by the School. Further details on School funding can be found at: View Website.

Person specification

For information

Essential

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.

Desirable

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

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

General

Applicants will be required to address the following.

  • 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?
  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Why do you believe you are suitable for doing Postgraduate Research?
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