AI Evaluation Exercise (Paid) by Agami X OpenNyai: Apply Now!
About OpenNyai
OpenNyai is a mission aimed at developing open source software and datasets to catalyse the creation of AI-powered solutions to improve access to justice in India. Artificial Intelligence is a new paradigm of teaching computers to do something by showing them examples. However, there are huge barriers to innovation for AI powered innovation to surface e.g. foundational components. These foundational components can be training and benchmark datasets for AI, baseline models for AI tasks and knowledge products that can enable many to adopt AI in their innovations and solutions.
About the Opportunity
Agami & OpenNyAI are looking for Lawyers or students with legal research experience to participate in a paid AI evaluation exercise. Â
In this evaluation exercise, you will be given 10 judgements and 3 different summaries of each judgement. Some of these summaries have been generated by an AI. You will evaluate how good a particular summary is in your opinion and provide feedback on what’s a hit and what’s a miss. You will be provided an honorarium of Rs 5000/- after the completion of the exercise.
Eligibility
We invite applications from candidates with Legal background. Applicants who have worked in litigation or legal research will be preferred. Understanding of Artificial Intelligence is NOT a prerequisite. Having professional work experience will be an advantage.
Application Procedure
To apply for this position, share your expression of interest and the link to your Linkedin bio by filling this form. Please note that we will only get back to you IF you fit our hiring criteria and qualifications within one week of your application.
Sample Task
Each participant will be provided with four documents:
- Original judgement text
- Two AI generated summaries of facts, issues etc.
- Human generated summary of facts, issues etc.
Each participant will read through all three documents and identify patterns of error and provide critical inputs which will be used to improve the quality of judgement summaries.
For detailed info, visit HERE.
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