Policy driven data science: using access policies and metadata to refine query workflows
The availability of large amounts of uncurated data of variable or unknown quality is a temptation and a risk for the data scientist, because it is almost too easy to generate results of questionable provenance and validity. A data analytics query induces a workflow that selects, transforms and joins data from a variety of sources, doing those operations that the query author specifies. Such operations need to be processed in the context of the consideration of two significant issues: is it permissable and is it correct to combine certain datasets? Or in longer form: are there access policies governing the use of the data and do dataset properties make them (in)compatible? A consequential question is to determine (automatically) the access policy and the dataset properties of any derived data. A simple yes/no is a start for the first two issues, but better would be proposals to support revisions to the query to produce equivalent results that respond to the intent of the query, while resolving or ameliorating access and compatibility constraints through reasoning and argumentation.
This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its second cohort of at least 10 students to start in September 2020. Further details can be found at: www.bath.ac.uk/centres-for-doctoral-training/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/.
Applicants should hold, or expect to receive, a First or Upper Second Class Honours degree. A master’s level qualification would also be advantageous. Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.
Informal enquiries should be directed to Dr Julian Padget: [Email Address Removed].
Enquiries about the application process should be sent to [Email Address Removed].
Formal applications should be made via the University of Bath’s online application form: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP02&code2=0002
Start date: 28 September 2020.
ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 tax-free per annum in 2019/20, updated annually in line with the GDP deflator) and a training support fee of £1,000 per annum.
We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.
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