Summary
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.
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| Web resources: | https://cordis.europa.eu/project/id/101069186 |
| Start date: | 01-08-2022 |
| End date: | 31-08-2024 |
| Total budget - Public funding: | - 150 000,00 Euro |
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Original description
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.Status
SIGNEDCall topic
ERC-2022-POC1Update Date
09-02-2023
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