Pay Equality Modelling

Pay Equality Modelling

Wage gaps between gender and ethnic groups receive a lot of media coverage and are a major issue in our society. Identifying and addressing them effectively and accurately shields companies from lawsuits and bad publicity, whilst promoting equality and fairness. For large organizations it can be difficult to identify these gaps as they are spread across thousands or tens of thousands of employees in a variety of roles, locations, and work experiences. Analyzing this data correctly is important as surface level trends don’t always tell an accurate story at the individual level.

 

In this case study, we created a machine learning model to predict expected wages and determine the extent of this international stationary retailer's wage gap. 

Document Classification: Automated Legal Patent Rejection

Document Classification: Automated Legal Patent Rejection

Reading and processing these documents takes upwards of tens of thousands of potential billable hours. It is in the best interest of firms to pursue the claims that are likely to result in a profit and set aside those that will not. Therefore, creating an algorithm that will accurately predict the outcome of legal patent applications automatically would save millions of dollars in opportunity cost.

Energy Usage Disaggregation

In this case-study, we disaggregate overall household energy consumption data into constituent appliance energy requirements. The benefits are threefold - 1) informs households of which appliances are using the most energy 2) personalised feedback to quantify savings for appliance-specific advice and 3) provides capability to build recommender systems to inform households of savings that can be achieved. In this particular case, SFL worked on data from a national European energy provider to expand their offerings.

Automated Classification of Images

A huge number of high quality photographs are being taken in the smartphone era. For many companies there is value locked within these images but the vast number of such images makes them tedious to sort through. Identifying features and objects in these images automatically allows companies to gather information in new ways and while expending fewer resources. This can be used both with publicly available images and with user supplied images, allowing companies to get trained eye on something without actually paying a trained employee to look at it.