How is AI Affecting your Job Applications – Part 1
What AI Screening is and isn’t, who benefits and who is disadvantaged?
In this 2-part series, we explain the role AI screening plays for job hunters and recruiters, and how to make sure you don’t fall victim to a flawed system when applying for a role.
At Yempo, we implemented AI resume screening to prioritise which applicants we should review first. Using the data from our 10 years of operation, we have been able to score applicants based on previous assessments and by comparing skillsets in their CV to the minimum requirements of the role. Using this data, we can assign an applicant a number from 1-100 which allows the recruitment team to sort and prioritise which applicants will be reviewed first. Note that we do not arbitrarily reject anyone.
Applicants given a low rating in this system are also reviewed regularly to ensure there are no false hits, but to date the process has proven highly accurate. Here are some examples of applicants that might be given a score indicating they are unsuitable for the role:
- A person with 3 years of call centre experience applying for a Marketing Manager position which requires minimum 2 years in a leadership position and 5 years marketing experience.
- A person with no work experience but who had an internship as a bookkeeper, applying for a role as a Full Stack Developer which has a dozen technical skills listed as desirable, and 4 listed as mandatory.
- A person with 10 years’ experience as a registered nurse applying for a position as a Management Accountant with a mandatory CPA qualification.
When Yempo first embarked on building an AI model to improve its recruitment processes, I thought we could tell the model what rules we wanted it to follow via program code, and it would follow them. For example, at Yempo we ask applicants how many years in the workforce they have, and how many different employers they have had. I wanted our AI model to “assign a lower rating in the points scale to anyone whose number of different employers is greater than the number of years’ experience.”
This was important to me because Yempo and its clients are seeking candidates who have demonstrated their ability to commit in the long term to a role. Our clients set the hiring principles, and we follow them. Applicants who change jobs annually typically don’t have the staying power our clients are looking for, so while I don’t want them rejected – there may be valid reasons for the movements across jobs that need to be examined – I don’t want them to be considered as recommended applicants.
Yempo’s Process Automation team explained to me that setting this rule for our AI model isn’t how AI works. Certainly, we could use automation to follow a set of rules and reject applicants based on them, which is a form of programmed discrimination, but not AI. They explained to me that our AI model was pointed at the recruitment team and their processes, and it monitored and then learned from their actions.
If Yempo’s recruiters had consistently rejected anyone over 45 years old, or rejected all female applicants, AI would have learned that and stored it for future use. Of course, it didn’t learn these things because they are ridiculous reasons to reject an applicant. Instead, it learned exactly, organically, how Yempo’s recruitment team works. It learned that if a candidate is earning P15k per month and their desired salary is P30k per month, they are likely not a fit for our vacancy for a senior tech specialist with a salary of P200k per month. It learned that if we are seeking an accountant with 10 years’ experience with a mandatory requirement of deep QuickBooks knowledge, and QuickBooks is not once mentioned in their CV, they are likely not a fit. These candidates are not rejected but deprioritized in the screening process. For this reason, it is vital that you pay attention to your CV and ensure it is accurate and answers the relevant questions and satisfies the requirements truthfully. More on this in Part 2 – How to get a better application score in the age of AI.
The Yempo AI model spent many months learning the work habits of our recruitment team, and concurrently we monitored whether the AI model would have delivered the same outcome. Over time, with an accuracy rating of 99.9%, we felt confident that we could turn on the feature and start properly and fairly prioritizing candidates.
What is the benefit to hiring companies that implement a model such as this?
Since turning on our AI screening model:
- We have saved a considerable amount of recruiter time as they are no longer reviewing applicants who just use the scatter-gun approach to job hunting – spraying their CVs for every possible role regardless of the requirements, regardless of their qualifications, experience and expertise;
- This in turn has increased the job satisfaction of the recruiters; they spend more time chatting to qualified candidates and endorsing them to clients;
- The hiring experience has improved for qualified candidates; we are able to process them faster and get them in front of clients for interviews;
- The client experience has improved because our recruiters are able to identify and endorse qualified candidates to them faster, and we don’t lose good candidates while wasting time reviewing unqualified applicants.
So who actually loses out if they are screened by an AI model that learned its rules from ethical recruitment practices?
Here are some examples:
- The 13,000 junior staff, mostly call centre agents, that sprayed their CV to apply for our senior vacancies even though they met none of the mandatory requirements.
- The 2,500 fresh graduates who applied for vacancies that clearly required deep levels of experience.
- That one guy who applies for every IT vacancy we’ve ever had for almost 10 years, even though he doesn’t meet any of the requirements, and who claims to have a monthly salary of P1m (USD $200k annually), far exceeding any salary we have ever offered.
Using AI in the recruitment process delivers benefits to a wide range of parties. It is important to understand how to make sure you don’t get inadvertently screened out of a role that you’re perfectly suited for.
Stay tuned for Part 2 – How to get a better application score in the age of AI.