Access to Justice for All, Leveraging AI Modeling
Access to meaningful justice, justice that is competent, affordable and achieves pragmatic outcomes, is missing in the U.S. Fact patterns that drive people and small businesses to seek the services of lawyers repeat daily. Matching the fact patterns to relevant legal concepts, precedents and procedures remains a bespoke process, almost hit-and-miss, where neither arbitration nor litigation assure timely or suitable remedy.
Our Access to Justice for All Working Group aims to tap into and build upon emerging advances in computer science, statistics and pattern recognition in order to preliminarily score and ultimately improve the viability of claims for redress that emerge from such fact patterns.
Such viability scoring will likely accelerate determining which legal theories are worthwhile, and how they are pursued by claimants, defendants, lawyers, legal aid organizations, government agencies and judges.
Website: https://j4all.org
For more information, contact:
- Bruce Cahan, organizer: bcahan@urbanlogic.org
- John Clippinger, co-organizer: john@fp1.ai
- Yen Kha, founding engineer, ykrunner@gmail.com
Project Lead: Bruce Cahan
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