Using AI to assess job eligibility: pre-employment assessment of learning agility
Some companies use AI technologies, such as machine learning models, to automate parts of the interviewing process that test for learning agility.
This is a person’s ability to adapt, learn, unlearn and relearn in the workplace; keep up with constantly changing conditions; and transfer knowledge that they have acquired from one context to another.
When individuals apply for a position at a company that uses these technologies as part of the interview process, they will receive a link that contains the interview questions. Interviewees will be required to either answer questions in writing (responding to a chat-bot) or video record their answers through the platform provided. If the interviewee video-recorded their answers, these will be transcribed using natural language processing techniques. All replies are then analysed with machine learning software.
The software looks for patterns in the interviewee’s responses that predict how likely they are to have the company’s desired skillset. When video recording is used, the interviewee’s facial and vocal patterns will also be analysed for subjective traits, like ‘conscientiousness’ or ‘openness’. The software will produce a report outlining how the interviewee scored, which will then be sent to the company alongside the interview data for review.
What are the benefits of this technology?
These tools promise a faster interview process. Not relying on a human interviewer allows multiple interviews over a set time period, enabling the company to consider a wider talent pool.
What are the risks of this technology?
Interview candidates may not be aware what personal data is being collected, for instance, eye movements and vocal patterns from video interviews. Using this type of data can be particularly disadvantageous to neurodivergent people, as the algorithms can read neurodivergent behaviours as distracted or uninterested.
In addition, while some AI based tools claim to have the ability to reduce bias, they may also increase bias against communities that are already discriminated against in traditional hiring processes.
What is natural language processing?
Natural language processing (NLP) is a branch of machine learning that aims to understand the structure and meaning of text, or to process language similarly to humans. There are several sub-branches of NLP, including natural language generation, which aims to have computers generate text that makes sense to humans; and natural language understanding, which aims to understand what a body of text means. Common uses of NLP are analysing text, converting speech to text, programming chatbots and translating between languages.
What is deep learning?
Deep learning is a subset of machine learning that uses neural networks with many layers — a method of processing data that has been modelled on the structure of the human brain, with many interconnected nodes organised in a layered structure. These neural networks are trained by automatically updating each of their layers as they are exposed to more data. Neural networks learn to make decisions and predictions through exposure to data, without being explicitly programmed to make specific decisions
What is machine learning?
Machine learning is a kind of AI. By learning from existing data (known as‘training data’), systems can identify patterns to make predictions or calculate probabilities. These systems are able to continuously adjust as they encounter more data. Based on what they learn from training data, they can perform a variety of functions, like playing chess, recognising faces or assessing welfare benefit applications.