A Fair Day's Work: Detecting Wage Theft with Data

The research team seek support to develop a set of data science driven tools to prevent the underpayment and exploitation of young workers (frequently referred to as 'wage theft'). The team are specifically looking for MDAP support in accessing and managing relevant datasets, and to prepare for the development of a new dataset, which will involve the use of natural language processing.

Young people (~15-24 years old) are especially vulnerable to wage theft, due in part to issues such as: a culture of wage theft in industries where young people make up the majority of employees; a lack of awareness among young employees of their workplace rights; reluctance to complain about exploitation because of fear of retribution, combined with lack of resourcing for proactive detection of non-compliance by the regulator. This last point, in turn, makes it difficult for regulators, unions, and other organisations to detect wage theft, let alone address it.

To address the disadvantage wage theft causes, the team proposes a multi-pronged approach that aims to first and foremost, support young people at risk of wage theft, while also providing data for regulators, policymakers and business to drive system change. The project will draw upon cross-disciplinary expertise in labour law and regulation, digital design, information science, UX design, data analysis and data ethics to design/develop three interlinked components: The Fair Day's Work portal; a Wage Theft Database and finally a Wage Theft Prediction Tool. At the core of these three components is developing a wage theft database from public and private datasets, and through a new dataset collected from employees and unions via the Fair Day's Work portal.