| Title | Determining Worker Type from Legal Text Data Using Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Yin, Yifei, Zulkernine, Farhana, Dahan, Samuel |
| Conference Name | 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) |
| Keywords | artificial intelligence, composability, Computational modeling, contractor, Data models, employee, Employment, employment law, Human Behavior, human factors, legal judgement prediction, machine learning, Metrics, Neural networks, pubcrawl, Scalability, supervised learning, text analytics |
| Abstract | This project addresses a classic employment law question in Canada and elsewhere using machine learning approach: how do we know whether a worker is an employee or an independent contractor? This is a central issue for self-represented litigants insofar as these two legal categories entail very different rights and employment protections. In this interdisciplinary research study, we collaborated with the Conflict Analytics Lab to develop machine learning models aimed at determining whether a worker is an employee or an independent contractor. We present a number of supervised learning models including a neural network model that we implemented using data labeled by law researchers and compared the accuracy of the models. Our neural network model achieved an accuracy rate of 91.5%. A critical discussion follows to identify the key features in the data that influence the accuracy of our models and provide insights about the case outcomes. |
| DOI | 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00084 |
| Citation Key | yin_determining_2020 |
Determining Worker Type from Legal Text Data Using Machine Learning