Inheir.AI
Experimental prototype for property inheritance and dispute management
Install / Use
/learn @inlibre/Inheir.AIREADME
InHeir.AI is an intelligent and secure legal tech platform developed to streamline property dispute management and analysis for legal professionals and local communities. By harnessing the capabilities of large language models (LLMs) and principles of privacy, the solution stands to ensuring data privacy, security and accuracy by adhering to responsible AI practices while ensuring responses are backed by established legal procedures. InHeir.AI aims to serve globalized requirements by providing a modular platform that can support tailored and updated legal establishments for catering to different needs beyond legal institutions.
Table of Contents
- Overview
- Why?
- Design
- Features
- Working
- Architecture
- Technologies Used
- Screenshots
- Challenges
- Impact
- Future Enhancements
- Proposal
- Contributing
Why?
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Cumbersome and expensive legal processes resulting in vulnerable population losing accessibility to legal procedures, inherently causing them to lose their titles and properties.
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Inadequate support for policy comprehension making legal policies harder to understand and leverage for less legally-aware citizens.
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Lack of means for reporting of vulnerable properties via crowdsourcing, resulting in lack of usable external data for property risk analysis.
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Absence of privacy-friendly, compliant system for the same, resulting in non-adherence to global regulations by usage of LLMs due to sensitive information.
Design
User-centric design
The core aspect of InHeir.AI is to be usable, clear, and accessible for end-users of all technical backgrounds for inclusiveness, which is achieved by user-centric design and interaction with user interface (UI), striking a balance in aesthetics and usability.
Modularity and comprehensive AI
The system is to be adaptable for different legal requirements and policies for improved global adoption. The system also aims to be comprehensive by handling different kinda of data using external knowledge base.
Responsible AI
InHeir.AI aims to be transparent, accessible, fair and privacy-preserving by adhering to Responsible AI practices to ensure marginalized communities are catered to without discrimination, all without having to trade privacy for convenience.
Streamlined Data Management
InHeir.AI aims to be a comprehensive platform for managing cases and property risk reports and providing relevant information by looking up on custom external knowledge base and data sources, providing geospatial insights and visualizations, aiming to be a hub for knowledge sharing to make property risk mapping more comprehensive in addition to publicly available data.
Security and Compliance
InHeir.AI is designed to be secure by design in order to maintain compliance with regulatory requirements on protection and usage of private data, empowering legal professionals to handle cases in a smart and efficient manner without sacrificing security by leveraging role-based access control. The system's meant to be deployed for own needs in order to have control over data processing.
Collaboration and Transparency
InHeir.AI's ultimate aim is to accelerate communities working in addressing property disputes, and one way to achieve it is by providing aggregated, synthesized data of property risk extracted from users of the platform for research and analysis, aiding in comprehensive dataset, serving as a single source of truth.
Features
Case creation and management
Users can create a new case by uploading their property documents (will, deed, probate) along with optional supporting documents (tax files, letters, etc.) processed by document summarization and ownership chaining for extraction of summary of data, containing information such as:
- Entities involved
- Properties mentioned
- Summary
- Type of case
- References
- Recommendations
for resolution of case by providing the above information in simple terms for understanding by common people. This is all done without sacrificing user's privacy or providing biased information by usage of grounded truths posed by generic laws.
Geo-spatial visualization
It is possible to visualize a physical address on a 3-D map with property risk information, which is useful for identifying if a property is blighted, at-risk, etc. For information on how this works, refer to GIS Analysis
Integration with Knowledge Base
InHeir.AI is designed to give grounded, factual, updated legal assistance and one way to ensure the responses are consistent and coherent. The system's to be provided with files on property laws on different countries (or specific if tailored towards citizens of specific demographic and nationality) for improved comprehension and accuracy in responses. For information on how this works, refer to integrated knowledge base
Standalone chatbot
Primarily several users would like to interact with a chatbot in independent manner from case. Despite creating and getting summary for the case, a user could still have some doubts. This can be clarified by leveraging the standalone chatbot which is responsible for performing RAG on property laws and on context and answering user's queries pertaining to the case and independent of it.
For information on how this works, refer to working of chatbot
Property reporting and management
Users can report on properties that have been abandoned, blighted, been tax delinquent, etc. which is a useful way for organizations to gather data regarding potentially vulnerable properties, something that was exhaustive and less comprehensive in the earlier days due to lack of proper outreach mechanisms. For information on how this works, refer to property reporting
Working
Document Summarization
- When a user uploads any document (primary or supporting document), the textual content of it is extracted via OCR after being written to Azure Blob Storage. This is done by Azure Document Intelligence.
- The textual content retrieved via Document Intelligence is passed to Azure AI Language for PII redaction and entity extraction for ownership chain modelling.
- The extracted entities are stored in a key map for temporary use case and query is made by usage of redacted text to ensure data's clean of sensitive information, which yields information based on the mapped anonymous entity. Let's say, an anonymized entity named ENTITY_1 is mapped to name John Doe, which means responses generated via LLM contains ENTITY_1. This is substituted back again on the server to send intelligible response to the client.
- The entities are stored in MongoDB with CSFLE (Client-side field-level encryption) to ensure entries are not accessible while at rest.
- The summary is provided to the user which is displayed in the dashboard.
Geographical Information System (GIS) Analysis
- When a user enters an address, it is sent to the backend for processing, where the address gets geocoded using geopy library's supported backend: OpenCage which can be freely accessed by an API key.
- The system then retrieves the coordinates in an accurate manner for the address and starts evaluating the property's attributes using OpenAI for web based access and public datasets available for Georgia by census.gov and [DOCP's data](https://dpcd-coaplangis.opendata.a
