Artificial Intelligence
Our team has been successful in getting an AISC award with the project AI-assisted LEgal Settlement (AILES): Automated e-Discovery and Judgment Prediction Using AI and Machine Learning. AI SuperConnector (AISC) programme https://aisuperconnector.com
The Early Career Researcher (ECR) to lead the grant is Ms Anna-Grace Linton (scagsl@leeds.ac.uk), a final-year PhD student in the School of Computing; the PI is Prof. Serge Sharoff (Sharoff@leeds.ac.uk) from the School of Languages, Cultures & Societies (AHC), and I am a Co-I. The project runs from 9th July 2024 until 30th June 2025. Even though a small scale, this grant is a pivotal part of a larger commercialisation project on Legal AI that Serge and I are pursuing in close collaboration with RIS/Nexus.
The recent success of generative AI in the form of Large Language Models (LLMs) like ChatGPT has led to the ability to produce realistic and coherent responses to various requests. The proposed opportunity, AILES, leveraging our research in AI explainability and data science, takes this technology to the next level aiming to transform legal proceedings, specifically targeting matrimonial cases such as divorces and family disputes. Our platform makes legal processes faster and more cost-effective by automating legal e-discovery and predicting court case outcomes. By analysing extensive legal documents, AILES assists lawyers in making quick and credible decisions. It predicts court outcomes and explains the reasoning behind these predictions, enhancing trust and transparency through explainable AI methods. Key benefits of AILES include:
- Amicable Resolutions: Promotes cooperative rather than adversarial operations.
- Anchored Expectations: Helps clients understand likely outcomes.
- Accelerated Outcomes: Accelerates the resolution process.
- Cost Savings: Reduces litigation expenses.
AILES addresses the major technical challenges in e-discovery by developing sophisticated algorithms capable of processing and analysing vast amounts of legal documents to identify the most relevant information.
As the final output, AILES will produce accurate, context-aware judgment predictions. Importantly, our algorithms ensure that the system’s recommendations are transparent and explainable. By integrating the strengths of LLMs and machine learning, AILES has a strong potential to set a new standard in legal practice, also extending to other areas of law. The disruptive nature of AILES could revolutionise legal operations, offering a smarter, more efficient approach to resolving legal disputes.
Another project we have been working on is commercialisation project “Forecasting Extreme Events in HealthTech: critical glucose level early warning system” which has just been submitted by our UoL start-up 4-Xtra Technologies Limited to Innovate UK under the call “New Innovators in Health Technologies, West Yorkshire”. If successful, the project will run from 1 Nov 2024 till 30 Apr 2025. Collaboration involves the Department of Statistics (c/o myself) and School of Medicine (through their Clinical Trials Research Unit, CTRU).
The 4-Xtra GlucoSentinel project will develop an innovative Glucose Level Monitoring Early Warning system focused on forecasting extreme events for diabetes patients and their medical advisors. Diabetes is a chronic condition that affects millions worldwide, requiring constant monitoring of blood glucose levels to prevent severe health complications. Current methods, such as finger-prick tests and continuous glucose monitors (CGMs), primarily provide current or historical data without predicting future extreme events like hypoglycaemia or hyperglycaemia, which can lead to medical emergencies. Our project aims to address this gap by building a proactive solution that forecasts such extreme events based on patient's real-time historical data and alerts patients and/or doctors in advance, thus significantly improving health outcomes and quality of life for diabetics.