To find out how to use Recruitment Automation to hire faster, reduce bias and save, we also have a great retail industry eBook on Ai in HR.
AI RECRUITMENT TOOLS
Artificial Intelligence. Machine learning. Chatbots.
While the possibilities of technology always felt like some distant future, there’s no denying that the future is right here and right now.
Every day, technology touches and enhances our lives in ways we rarely even pause to think about. Algorithms, apps and digital automation continue to reshape the ways we shop, connect, bank, get around or even track our fitness and the steps we walk each day.
It’s changed the ways we access customer services and the ways we can connect with our tribes across social platforms. And in the time of COVID-19, it’s enabled ways of efficient remote working that few thought could be possible.
With the uptake of automation and artificial intelligence (Ai) across every industry sector, it was inevitable that these technologies would reshape the HR and recruitment domains too. Compared to manufacturing automation, service delivery, supply chain management and marketing channels, HR and recruitment might be a little slower on the uptake. Ai tools are now rapidly reshaping the essential functions of hiring.
Employing the latest advances in Ai, machine learning and big data practices are delivering new efficiencies and better outcomes for businesses, recruiters and candidates alike.
Conversational Ai is a type of Ai that lets businesses have dynamic and meaningful conversations at scale with customers, staff, business partners and candidates.
Conversational Ai uses Natural Language Processing (NLP), a sub-field of Ai that’s focused on enabling computers to understand and process human languages. Through machine learning, it aims to get computers closer to a human level of understanding of language.
Conversational Ai uses NLP to discern meaning from both written and spoken word:
Sometimes referred to as chatbots or textbots, Ai-based conversational tools continue to evolve and be applied in new and extraordinary ways. Through NLP, the ability to read, decipher and understand written and spoken language has evolved to the point that personality traits, sentiment and other inherently human characteristics can be understood from written and conversational exchanges alone. Our own peer-reviewed research shows how personality traits can be accurately inferred from answers to standard interview questions captured via a text chat.
Ai recruitment works best in high volume recruitment such as customer-facing retail or service team roles. In roles and industries with fewer candidates or more senior positions to fill, traditional recruitment practices are likely to be preferable.
Conversational Ai can be helpful for profiling personalities in candidates or existing employees without the time and costs of conducting lengthy psychometric profiling. Add video into the mix and machine learning can add additional layers of meaning through analysis of facial expressions and profiles, body language and more.
Video interviewing continues to divide opinion as many believe it allows for unconscious (or not so unconscious) bias to remain front and centre of the hiring process. In text-based Ai interviews, many of the usual bias cues or triggers an be effectively eliminated at the candidate screening stage.
In a post-COVID or COVID-normal economy, employment opportunities will be competitive. As more people compete for potentially fewer jobs, finding and engaging the best candidates will be even more challenging.
Ai-powered interviews can help recruiters cast their net wider to reach a bigger pool of candidates and find better-qualified candidates.
People know text and are comfortable with text. So by providing a text chat-based mobile-first experience for candidates, improves the user experience and addresses communication challenges.
Chat-text provides an easier and less confronting interview process for many candidates.
Everyone has a story that’s bigger than their CV and Ai recruitment interviews give every candidate an opportunity to tell theirs. Candidates can choose when and where they complete their interview and standardised interview questions ensure a level playing field for all candidates.
Sapia’s text chat interview automation is blind screening at its best. We’ve removed possible factors that can influence human bias – no CVs, no socials, no videos, no facial recognition and no time limit. It’s just the candidate and their text answers, providing a fairer and richer experience where candidates feel comfortable just being themselves.
One of the most well-known applications of Ai, data science and machine learning is Recommender systems or Recommender engines. It’s how Spotify suggests the track you might like next. Or how Netflix recommends your next binge-worthy series. And how Amazon recommends books or products likely to be of interest.
In hiring, Recommender Systems use predictive modelling to recommend the most-likely best matches of applicants for a role.
Recommender systems guide decision-making by using machine learning to analyse all the data available through the HR lifecycle. From job advertising and clicks, through interviewing and hiring, to employees’ job satisfaction and tenure, data can be analysed to reveal predictive patterns and insights.
Data can find connections that humans don’t, providing valuable insight into what an ideal candidate looks like or where you’re likely to find them.
Recommender systems can cut through the ‘noise’ by providing a shortlist of top-ranked candidates. This is without burning time, sorting and reviewing potentially hundreds or even thousands of applications. Predictive intelligence shares additional insights on candidates’ values, traits, personality and communication skills. It helps to simplify the selection and guide faster talent decisions.
Machine learning is not infallible. One important consideration is questioning whether the data being used is not inherently biased. If, for example, machine learning models are built around data from a workforce that historically skewed towards male, the recommendations will inevitably have a male bias. Machine learning should only guide a decision not to make it and, ultimately, it’s always important to have real people making decisions about people.
Reviewing CVs of all candidates can be the most time-consuming part of a recruiter’s job. Especially for large-scale briefs such as retail or customer service teams. In defining a shortlist of potential candidates to proceed to the interview stage it can be hard to differentiate between CVs. It’s also easy to make decisions that may be based on personal biases.
But what if you could start the hiring process with all the benefits of an interview process, without investing your time in them? And what if in the time it would take to properly review just a handful of CVs, thousands of candidates could be screened by interview?
With Ai recruitment tools you can.
When it comes to recruiting and hiring, the ability to read the mood as well as the words is a game-changer in candidate assessment. Here are our top five benefits for your business:
Without even having to consider CVs upfront, an upfront screening interview reduces time to hire by providing a shortlist of candidates with the best fit to move forward.
Ai interview automation looks beyond the CV to assess the skills, traits and temperament of candidates. Based on past hires, Ai learns what a successful candidate for your business looks like and joins the dots to find others that match that profile.
Recruiters and hirers can save time reviewing and assessing CVs. With the ability to complete briefs faster, build teams sooner and achieve business metrics, you can be on to the next job sooner. Or free yourself to concentrate on what you do best: building relationships, delivering a better hiring experience or enhancing the onboarding process.
Ai-enabled interviewing helps reduce the effects of unconscious bias – the inherently human prejudices, personal preferences, beliefs and world-views that shape our assessment of others. Our biases can easily have a negative impact on candidates and mean you’re potentially missing out on the best candidates for the job. It can also mean employers are missing the opportunity to cultivate workplace diversity and all the benefits it delivers.
Diversity improves employee productivity, retention and happiness. Time and again, research shows that diversity – of background, gender, experience and more – improves employee productivity, tenure and job satisfaction. In 2020, global management consulting company McKinsey confirmed that “The most diverse companies are now more likely than ever to outperform non-diverse companies on profitability”.
Companies that have automated part of their candidate screening and interviewing are not only reaping the benefits of a more streamlined and stress-free process but report an immediate pay-off in time and efficiency savings.
Get your time back quickly and reallocate budgets towards higher-value investments and automation in other areas of recruiting.
Use Sapia’s free calculator to:
Everyone has one part of their job that they could do better if they had more time. Like managing stakeholders. Improving business partnership skills. Or networking to improve talent pools with a focus on those high-end and hard-to-fill roles. Whatever yours might be, interview automation can give you back time to focus on high-value tasks.
Reviewing CVs and managing interviews might not be the biggest challenge in your role, but they are likely to be the most time-consuming. Automate those upfront interviews using the tools and process of Ai recruitment and you can focus on the bigger picture of finding the best fit for every role and meeting every brief with confidence.
While this one’s last on our list of the benefits of Ai interview automation, it could equally be the most important.
Ever since job boards hit the market, recruiters have been inundated with candidate applications. While that’s been good news for potential employers as well as recruiters, it’s not so good for candidates. Too often candidates make the effort to apply for a position, but then due to the sheer volume of applications they never hear a thing from the recruiter or hirer. It’s called “ghosting” for obvious reasons.
Ghosting is not just a bad look, it can be bad for business. Candidates can easily share a negative experience on social media. They may also be less inclined to apply again or accept a job offer now or in the future.
With interview automation, you can turn every candidate engagement into an efficient, empowering and enjoyable experience.
Sapia’s award-winning interview automation offers a mobile-first, text chat interview. At scale, it delivers an engaging and relatable, in-depth interview, followed up with personalised feedback for every candidate. Here’s how Ai automation provides a superior experience to a traditional interview process:
Find out more about Sapia’s Ai-powered recruitment tool and how we can support your recruitment needs today.
Transcript (Barb Hyman):
We really do not want to lose the human touch.
I think that is what I hear the most when I talk about AI to people: that recruiters, hiring managers, and organizations are terrified that bringing AI in will somehow make them less human as an organization, and that they will lose people as a result.
I actually think that we’ve all been thinking about it in the wrong way. So think about it like this.
Do you really want to be spending all of that really expensive and valuable time and capacity with people that you may not hire? Do you want your leaders or recruiters investing that time?
Of the 30 people that you bring into an assessment center, or the 10 people that you bring through to the phone screen stage – are those really the ones where you want to be investing the human touch?
I don’t think so.
I think where you want to invest and show a crazy amount of love and attention is to those who you want to hire, which is at the point of hire.
That feeling when you get extended an offer and the hiring manager calls you and says, “Wow, we are just so excited that you’re going to join our organization.”
When they say, “What do I need to do to get you across the line?”
That’s when it really matters.
And I think that actually is where companies are doing at the least.
So instead of being worried about the human touch, think about where it matters the most – that it really isn’t something to invest in up until the point of you’ve made your decision.
I’m not saying that you would rely entirely on AI up until that point, but frankly, candidates aren’t looking for it.
They want to get a job and they want efficiency as well.
And the best place, the right place for you to invest in all that human love and attention is on those to whom you’ve actually made the decision to extend them an offer.
The rise in video platforms for hiring suggests we still have as strong a ‘bias’ towards having to see someone to hire someone, as there has been with having to see someone working in the office to trust they are working.
What will it take for that bias to be disrupted?
Mature organisations who have fully remote teams working in 75+ countries, hire remotely via text and/or email. No face-to-face and definitely no video interviewing, which can be a petri dish for bias.
Many companies are hurting right now. COVID-19 is forcing them to make lay-offs and tough decisions about the things that mattered to them. For some, Diversity and Inclusion initiatives have been the first to go. Given the havoc that COVID-19 has created in our economy, this loss of focus is somewhat understandable.
Then George Floyd died after a police officer held him down so he was unable to breathe. In the week since we’ve seen unprecedented statements coming out from companies in support of the #blacklivesmatter movement. This signifies a huge shift in how companies engage with these issues, but when we’re fighting institutionalized racism, and corporate America is a very much part of the institution, it doesn’t matter how powerful your statement is – unless you’re unwilling to take action and to change internally.
The idea of “blind applications” became a thing a few years ago, with companies removing names on applications thinking that it would remove any gender or racial profiling. It made a difference, but bias still existed though the schools that people attended, as well as the past experience they might have had. Interestingly, these are two things that have now been shown to have no impact on a person’s ability to do a job.
Artificial Intelligence was touted as the end-solution, but early attempts still ran through CVs and amplified biases based on gender, ethnicity, age – even if they weren’t recorded, AI created profiles comparing ‘blind’ candidates to those in roles currently (ie. white men) – as well as favouring schools and experience.
True bias in recruiting can only exist if the application is truly blind (no demographics are recorded) and is not based on a CV, but through matching a person’s responses to specific questions to their ability to perform a job. It has to be text-based so that true anonymity can be achieved – something video can’t do as people are still racially profiled.
To have a conversation about removing bias from your organisation – we would love to chat
Have you seen the 2020 Candidate Experience Playbook? Download it here.
There are some steps we can take to eliminate bias in recruitment and it begins with not relying on CVs as a method of evaluating candidates.
CVs are full of information that is irrelevant to assessing a person’s suitability to do a job. They instead highlight things that we often use to confirm our biases, and draw our attention from other key attributes or aptitudes that might make someone especially suitable for a job.
For example, if a CV mentions a certain university it might pique our attention (a form of pedigree bias). This is problematic, as there may be socio- economic reasons why someone attended a certain university (or did not attend another) and CVs do little to reveal this. Situations like this confirm the bias that lead to it in the first place, compounding bias for these long-term systemic issues.
Additionally, CV data reduces a candidate pool in a way that is not optimising for better fits for the role, by relying on the wrong input data and criteria to find a candidate. Amazon discovered this when it abandoned its machine learning based recruiting engine that used CV data when it was discovered the engine did not like women.
Automation has been key to Amazon’s dominance, so the company created an experimental hiring tool that used artificial intelligence to give job candidates scores ranging from one to five stars.
The issue was not the use of Ai, but rather its application. Amazon’s computer models were trained to vet applicants by observing patterns in resumes submitted to the company over a 10-year period. Most came from men, a reflection of male dominance across the tech industry. As a result of being fed predominantly male resumes, Amazon’s system taught itself that male candidates were preferable. It penalised resumes that included the word ‘women’ as in “women’s chess club captain.” It also downgraded graduates of all-women’s colleges.
Studies have shown systemic unintended bias occurs when reviewing resumes that are identical apart from names that signify a racial background or gender, or a signifier of LGBTQIA+ status. The solution for this has been to remove names or any identifiable data from an interview or CV screening, but these have still experienced bias issues like those discussed earlier.
In order to be truly blind, any input data needs to be clean and objective. This means that it gives no insight into someone’s age, gender, ethnicity, socio-economic standing, education, or even past professional experience.
To truly disrupt bias, recruiters and hiring managers should utilise a new wave of HR tech tools such as Sapia, stepping away from using CV data as a way to determine job suitability.