NLP Series: Natural Language Processing and Inclusion
We continue our blog series on Natural Language Processing with an article from the team behind Scismic, Dr Danika Khong and Dr Elizabeth Wu. Scismic is a tool designed to remove bias from the recruitment process in the life sciences, to create a more diverse and representative workforce . The founders both hail from Boston, USA. In April 2019 Scismic won a Catalyst Grant for the beta version of their product, and last month Digital Science welcomed Scismic into their family of portfolio tools to help make research the best it can be. In this post, we will hear more about what Scismic does, and how and why the team hope that they will soon be able to implement NLP techniques into their processes.
What is Scismic?
Scismic Job Seeker is an online hiring platform for scientists that works toward accelerating therapeutic development by delivering the people best suited to specific research positions. As part of our efforts, we are working to reduce human biases in the candidate evaluation process, which has been shown to exclude scientists of non-traditional backgrounds from the recruitment pipeline. Scismic was recently awarded a grant from the National Institutes of Health in the US to further develop the matching system towards increasing the number of underrepresented scientists invited for job interviews. As we build out our system, we are exploring the incorporation of natural language processing (NLP) to enhance our matching system.
How can Scismic help reduce or eliminate unconscious bias?
Scismic uses a skills-based matching algorithm to ensure candidates are being assessed on non-biographical qualifications. In the US, the Equal Employment Opportunity Commission (EEOC)’s Uniform Guidelines on Employment Selection Procedures stipulates that the outcome of hiring assessments should be based on 3 forms of evidence. Criterion validity is one form of evidence encompassing predictive ability of the assessment to job outcome (e.g. skill sets). With its skills-based matching algorithm, Scismic bypasses the need for the preliminary human screening of skills. In addition, Scismic’s system is also able to translate the words scientists use for their skills to the words talent acquisition teams use to describe the same skills. Scismic achieves this using a skills taxonomy to ensure that the contextual definitions of individual words can be preserved during this translation process. This comprises of words manually linked or strung together to define a precise scientific skill set, each carrying a defined functional importance within the field of life sciences. Matching these skill sets between job seekers and companies using Scismic’s taxonomy establishes the criterion requirement while reducing human bias, and enhancing both job seeking and staffing efficiencies.
The Scismic recruitment process
How Scismic works
As Scismic scales towards wider and more diverse audiences, the manual efforts in building and maintaining this taxonomy will no longer be sustainable. Naturally, Scismic is preparing to turn to NLP to emulate recognition of these skill sets, their functional role in the life sciences, and therefore their relationships to each other.
NLP exists to make sense of the human language, which has vast potential to improve technical systems across industries. The challenging aspect of this is in the semantic analysis of words; i.e. the understanding, and hence preservation of the meaning and interpretation of the words and how sentences are structured. Scismic strives to build a more sophisticated taxonomy of skills that takes complex and highly specific scientific terms and translates them to simple but functionally similar terms through this AI tool.
Implementing AI and machine learning into an existing process
Incorporating AI is not without risk. One danger is the unintentional creation of bias within the system due to population biases among its users. Scismic will analyze strict metrics to proactively test its system for biases in order to evaluate and address potential biases within the matching system.
For Scismic, the adoption of NLP promises scale without compromise to its match accuracy. We walk down this road, knowing that the process will not be easy and will require frequent assessments that validate the incorporated AI for accuracy with little analytical bias.