Explainable, Ethical, Green Artificial Intelligence

About the Project

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying.

This project will focus on the development of Explainable AI methods that require little or no human interaction (fully automated) and can self-adjust their parameters and that will have the potential for reasoning capability. In addition, a major focus will be on the Green AI potential of the approaches to address the increasing energy demands of AI in our over-heating world.

With the advanced capabilities of computational resources, researchers have managed to develop complex algorithms that can produce unprecedented results across various tasks. Depending on the problem, some algorithms utilise enormous resources for days or months to complete their task or to achieve a competitive performance. For example, the training of a deep learning model on policy document on 8 NVIDIA P100 GPUs requires 274,120 compute hours and consumes about 656,347 kWh of energy. This is equivalent to the consumption of 226 domestic electricity meters in the UK per year. This has brought attention on the tremendous energy that is required to run these approaches, and on the study of their negative impact on the environment due to the produced greenhouse gas emissions and resulted in international events and assemblies (more recently the 26th UN Climate Change Conference of the Parties (COP26) in Glasgow), with international agreements such as the Paris Agreement which aims to achieve “energy-related carbon dioxide emissions to net zero by 2050”.

In light of this, the study of environmental cost of Artificial Intelligence approaches, more specifically in machine learning algorithms, has been a trending research line and has brought the attention of many researchers and practitioners in the community.

The growing complexity of machine learning algorithms raise other concerns that are related to their opaque nature and the lack of transparency. Many AI approaches, for instance the ones that fall under the deep learning umbrella, can achieve impressive results in terms of their Performance, however, their internal interactions cannot be understood. This has become a growing concern and has triggered the need to clearly understand the automated decisions that are made by these methods, more importantly, if they are intended for use in some critical or highly sensitive domains, such as: health, transportation, finance/credit scoring, human resource, criminal justice, public safety (terrorist detection), and so on. To what degree can we trust the AI decisions that can be biased or erroneous, and complex or opaque (difficult to comprehend)?

Justifying algorithmic outputs, more importantly when something goes wrong, is one of many reasons why we need to open the black-box of AI. Having the ability to clearly understand the outputs and decision making process made by the AI can help to improve the methods towards better more ethical outcomes.

Recent research work at the University of Aberdeen focusses on the aspects of Explainable, Ethical and Green AI in the domains of: textual analysis and automated topic detection [1,2]; automated data mining methods [3,4]; and Autonomous learning in Robotics [5,6].

This PhD will look to further the research in this exciting and increasingly relevant area.

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in Computing Science, Engineering or related disciplines

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for PhD in Engineering to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project titleon the application form. If you do not include these details, it may not be considered for the studentship.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you do not need to provide a research proposal with this application.

Informal enquiries can be made by contacting Dr A Starkey at a.starkey@abdn.ac.uk. If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at researchadmissions@abdn.ac.uk

Funding Notes

This is a self-funding project open to students worldwide. Our typical start dates for this programme are February or October.

Fees for this programme can be found here Finance and Funding | Study Here | The University of Aberdeen

References

[1] Short Text Classification using Contextual Analysis. Al Sulaimani, S.H., & Starkey, AJ. Nov 2021, In: IEEE Access. 9, p 149619-149629
[2] Predicting Supervised Machine Learning Performances for Sentiment Analysis Using Contextual-Based Approaches. Abdul Aziz, A. & Starkey, AJ, Jan 2020, In: IEEE Access. 8, p. 17722-17733 12 p
[3] Review of Classification Algorithms with Changing Inter-Class Distances. Akpan, U.I. & Starkey, AJ. Jun 2021, In: Machine Learning with Applications 4, 12p, 100031.
[4] Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM). Starkey, AJ, Ahmad, A. U. & Hamdoun, H. 6 Nov 2017 In : IOP Conference Series: Materials Science and Engineering. 261, 1, p. 1-7
[5] An Unsupervised Autonomous Learning Framework for Goal-directed Behaviours in Dynamic Contexts. Ezenkwu, C & Starkey, AJ, Jun 2022, In: Advances in Computational Intelligence 2, 14
[6] Unsupervised Temporospatial Neural Architecture for Sensorimotor Map Learning. Ezenkwu, C. P. & Starkey, AJ, Aug 2019, In: IEEE Transactions on Cognitive and Developmental Systems.