Objective
MultiDoor is a digital platform based on conflict resolution and machine learning expertise to address the comprehensive needs of litigants and recommend their best way forward to resolve their disputes. At present, litigants attempting to navigate through the civil justice system end up drifting through an incoherent, opaque process generally resulting in some form of reluctant compromise. While court systems worldwide are investing much effort to increase efficiency, a human-centred approach, which takes into account litigants' needs, interests and emotions, is lacking. MultiDoor employs an innovative intake screening recommendation system to integrate each litigant's (or potential litigant's) specific needs, interests and emotions, the features of the case, and the predicted case trajectory in the legal system, resulting in a diagnostic recommendation (e.g. mediation, arbitration, adjudication, out-of-the-box solutions). We describe the activities needed to develop a beta version of MultiDoor, including conceptual framing and validation. The activities include a crowdsourcing experiment to accumulate data on users’ satisfaction with conflict resolution-oriented processing of their disputes; developing forecasting models for user satisfaction; and developing a machine-learning based recommendation system. MultiDoor’s benefits include: 1) developing a new domain of conflict resolution machine learning via collaboration among data scientists and legal and conflict resolution experts; 2) advancing a personalized conflict resolution-oriented response to disputes, including to small non-litigable disputes; 3) promoting public trust and social wellbeing by ensuring that parties – including those from disenfranchised sectors – are informed and supported to self-determine how to resolve their disputes; 4) answering the current drawbacks of Online Dispute Resolution (ODR) systems, which focus mostly on legal issues rather than on the interests of the parties.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2022-POC1
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
52900 Ramat Gan
Israel
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.