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These are 2018 projects. We're not accepting applications for these projects.

Project 1: Algorithmic Dispute Resolution in ConstructionsConstruction

  • Project Description: Construction is the second largest global industrial sector. Litigation accounts for approximately 10% of the expenditure. The industry suffers from a dysfunctional relationship between the Architects, project managers, consultants, developers, and clients. The use of Blockchain and AI technologies has been investigated by the CBC @ UCL and identifed numerous areas or themes where a disruptive effect can be achieved. Legal, in particular contracting and dispute resolution are one of the main pillars of this project.This is a phased project which will model the workflows of a major construction project, in partnership with a leading UK contractor/project management company. The aim is to identify all relevant material prior to the contract being signed, automating the discovery phase of litigation, machining the large data set down to a 'hearing bundle' and then assessing 'needs and interests' prior to an automated resolution process. This is the first phase of the project which will identify work flows and identify the relevant documents, files and other digital material and assemble them in the blockchain where authentication can take place and a 'hearing bundle' prepared .

  • Additional Information: This is a project to build a demonstrator of a project that arises out of a joint paper by Jeremy Barnett and Philip Treleaven ' Algorithmic Dispute Resolution:The automation of professional dispute resolution using AI and Blockchain Technologies'https://academic.oup.com/comjnl/advance-article-abstract/doi/10.1093/comjnl/bxx103/4608879. See also https://www.constructionblockchain.org for full details about the CBC and www.Resilience-Partners.co.uk for more information about smart contract and other key issues.

  • Learning Objectives: The intern will develop skills in

    • construction contracting

    • a comprehensive approach to digital information in the construction sector including BIM ( Building Information Modelling)

    • The use of AI to identify relevant documents from a large data set.

  • Expected Outcome: The intern would be expected to prepare a report on the achievements of the individual modules in the project during the course of the internship and make proposals as to how the objectives of the project could best be implemented going forward to the next stage.

  • Eduction Level: Graduate student preferred

  • Desirable Skills: Knowledge of Construction Contracting and/or coding for smart contracts

  • Level of Difficulty: Challenging

  • Preferred Hours and Length of Internship: Part-time (20 hours a week for 24 weeks starting in summer and ending in fall)

  • Mentor(s): Jeremy Barnett, Jeremy.Barnett@Resilience-Partners.co.uk

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