Deriving causal factors from a large scale decision corpus

AI_CDT_DecisionMaking

Details

There is a huge amount of published literature describing tens of millions of evidence trials for various aspects of human endeavor, ranging from medicine to conservation to socioeconomic development. With modern machine learning, we now have the opportunity to analyse this literate at scale, and identify causal factors across multiple trials under seemingly diverse circumstances. For example, in the case of conservation evidence, we can identify experiments about one particular intervention (“reducing pesticides to prevent bee colony collapse”) and measurements (“pesticide levels”), but identify other possible causal factors around (“microplastics from a nearby recycling facility”) causing the pollutant. By running LLM-structured searches across the full body of literature which contains many trial results, we can now identify many more potential causal factors to help with further hypothesis generation and testing. Our initial focus is on conservation evidence (conservationevidence.com), but we plan to expand into other areas such as education effectiveness and socioeconomic interventions. A key goal is to construct a usable interface to these searches such that non-expert policymakers can access this capability without programming or machine learning experience. Data availability. This sort of analysis is possible due to a new corpus of literature we have assembled in Cambridge, in collaboration with the Office of Scholarly Communication, to download the full texts and metadata for millions of academic papers (including non-open access ones). We will conduct the causal analysis across this dataset, and also include further “grey literature” and eventually widen the document corpus to non-scientific literature (such as planning and policy documents).

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project. For any questions please contact the UKRI AI Decisions CDT Team (aidecisionscdt@manchester.ac.uk).

How to apply:

Please apply through the below link for the PhD Artificial Intelligence CDT:

https://pgapplication.manchester.ac.uk/psc/apply/EMPLOYEE/SA/s/WEBLIB_ONL_ADM.CIBAA_LOGIN_BT.FieldFormula.IScript_Direct_Login?Key=UMANC1251000021489F

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language Certificate (if applicable)

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. We place major emphasis on the importance of team work and an enjoyable work environment as a foundation for performing internationally leading research. This will allow the student to acquire cutting edge research methodologies in a supportive environment, where they can focus on making the best possible scientific progress.