Good pattern recognition allows you to make better decisions, short-circuit lengthy processes, avoid mistakes, and better understand risks.
But it has a downside too. Just because you can see a pattern in what has gone before, it is no guarantee that those same things will be true in the future.
Pattern recognition produces particularly flawed results in the hiring process.
When you hear hoofbeats, it’s probably horses. But you never know when it might be a zebra.
We all want to hire people like us, but true innovation comes through diversity.
Recruiters know that they should strip-out any markers that trigger unconscious bias when interviewing – but unconscious bias is hard to fight. The only way to remove those markers is via technology.
AI helps you discover the right patterns without bias.
Every role has a unique profile and every person has their own unique personality and aptitude DNA. We use a combination of natural language processing (NLP), a branch of AI-specific to text data and machine learning to predict with 85%+ accuracy if someone is right for a role.
NLP provides methods to program computers to process and analyse large amounts of human language data. It takes many forms, but at its core, it’s about communication, but we all know words run much deeper than that. There is a context that we derive from everything someone says.
Google, Facebook, IBM Watson are technologies that also rely on NLP to comb through large amounts of text data. The end result is insights and analysis that would otherwise either be impossible or take far too long.
Women are more conscientious than men in their text interview.
Men make on average 4.5% more language errors than women while taking 2% more time on average than women.
Interestingly men show higher levels of English fluency using more difficult words than their female counterparts, more than 4.5% on average.
These stats fluctuate depending on the role. For example, when applying for customer service roles, women take 6% more time than men while making 5% fewer language errors (language errors include grammar and spelling errors).
Women use more words on average in their text interview than men. We don’t find this to be the case.
Who writes more depends on the role family, but we find the difference to be +/- 2% on average (effect size, a more accurate way to measure the difference in averages is less than 0.2 across all role families. This is considered small). For example, in Graduate roles, men write more and in sales and hospitality roles females write more, while answering the same interview questions.
Our data shows that more extraverted candidates are preferred at the hiring stage for sales roles.
On average a hired candidate is 7 percentile points higher in extraversion than the candidate population average. As we track new hire performance in their first 12 months and beyond, we are starting to see a different profile turning up in the better sales performers – more introverts.
If you want to learn more about how we get to these insights from our FirstInterview AI screening tool get in touch here.
In sales, your single-minded focus on targets is far more important than how you present yourself. For recruiters who think otherwise, they may be operating with bias.
It’s been an exciting start to 2021 for Sapia (Formerly PredictiveHire) and I’m pleased to share that we have been nominated as one of six global HR Tech startups to watch, by the HR Technology Conference and Expo, which is taking place this week.
I’m extremely proud of my team for achieving this feat – it’s been a team effort, and after several months of implementing new processes and initiatives, it’s a wonderful accomplishment. I am thrilled that Sapia has been recognized as one of the leaders in the industry by the HR Technology Conference this year.
Sapia was among six companies chosen from 24,000 applications to have the honour of presenting. It gives us the opportunity to showcase our technology to over four-thousand viewers that will be tuning in over the week. It’s a huge honour to be showcasing how our Ai-enabled chat technology can truly change recruiting.
We launched our exclusive Ethics Charter called FAIR earlier this year; a call to arms commitment that includes a guarantee towards inclusivity, fairness for all, explainable AI, transparency, privacy policies and accountability. We also recently commissioned exclusive research with Aptitude Research to uncover global company attitudes towards automation, technology in talent acquisition and unconscious human bias.
We’ve hit several milestones when it comes to evolving our offering for the better, and presenting at the conference this weekend is the cherry on top. Our biggest priorities right now are raising awareness of the importance of ethical AI and abolishing unconscious human bias. The world right now is at a stage where this is critical for the success of companies of the future and we’re proud to be discussing this and more at Friday’s session.
Sapia will be featuring in a session on innovative HR tech startups on Friday March 19 at 2:00PM ET.
To register for the virtual webinar, guests need to enter their details via this link: https://blog.hrtechnologyconference.com/hr-technology-conference-exposition-spring-set-to-explore-industrys-startup-ecosystem.
More about Sapia
Sapia is a frontier interview automation solution that solves three pain points in recruiting – bias, candidate experience, and efficiency. Customers are typically those that receive an enormous number of applications and are dissatisfied with how much collective time is spent hiring.
Unlike other forms of assessments which can feel confrontational, Sapia’ Chat Interview™ is built on a text-based conversation – totally familiar because text is central to our everyday lives. Every candidate gets a chance at an interview by answering five relatable questions. Every candidate also receives personalised feedback (99% CSAT). Ai then reads candidates’ answers for best-fit, translating assessments into personality readings, work-based traits and communication skills. Candidates are scored and ranked in real-time, making screening 90% faster. Sapia fits seamlessly into your HR tech-stack and with it you will get ‘off the Richter’ efficiency, reduce bias and humanise the application process. We call it ‘hiring with heart’.
A job interview is often an intimidating experience for a candidate, but it needn’t be this way. There are ways that companies can make interviews a comfortable process for the candidate, more effective at getting the right data to make decisions, and reduce biases that can disadvantage members of under-represented groups.
Interviews need to be structured and ask the same standard questions of everyone, making them applicable to the type of role you’re filling. Questions need to be open-ended that permit more than one answer, providing an opportunity to see how candidates think through problems and solutions. Questions shouldn’t be written to be as “gotchas,” but rather give people an opportunity to be themselves.
We’ve talked at length about bias when doing initial screening, but this is something that traditional style face-to-face first interviews also don’t solve for. It is possible for interviews to be ‘blind’ and free from bias as well.
This requires removing visual biases – those based on what we see – from an interview process. This is made possible through the use of text-chat as the preferred method of interview. It’s something that many successful companies like Automattic (the makers of WordPress) have done for years.
Texting is something that most people are familiar with. Ai-enabled text chat feels very similar to texting a friend. Text chat is how we truly communicate asynchronously – we all do it every day with our friends and family. It needs no acting; we all know how to chat. Empowered by the right AI, text chat can be human and real, it is blind, reduces bias, evens the playing field by giving everyone a fair go and gives them all personalised feedback at scale. It can harness the true power of language to understand the candidate’s personality, language skills, critical thinking and much more.
We know we can get this right because at Sapia where we use chat-enabled Ai we send every candidate who uses our platform feedback on their interview, identify their strengths and weaknesses and help them understand what they might improve on. Thousands of messages a day confirm we are accurate (98%) of the time.
An inclusive interview process doesn’t exclude anyone from having an interview. This is something we are able to offer at Sapia. Everybody gets a chance at interviewing for the job. Everyone gets a fair go.
To find out how to improve candidate experience using Recruitment Automation, we also have a great eBook on candidate experience.
By Jennifer Hewett, Australian Financial Review, 31 January
The online questionnaire wants to know whether I respect and comply with authority. I get five options – strongly agree, agree, neutral, disagree or strongly disagree. I tick “neutral”. Well sort of, sometimes, I think to myself.
Same choice for whether I am good at finding fault with what’s around me at work. I tick “neutral” again, guiltily acknowledging it’s just possible my editor might have a different opinion about whether I am far too good at that particular skill.
The choice seems less ambiguous when I am asked whether I forget to put things back in their proper place. I hover over “strongly agree” or “agree” and tick the latter – perhaps a little optimistically.
And on it goes for 90 questions, with slight variations in the possible answers, as devised by an AI (artificial intelligence) algorithm. My responses to the bot will determine whether I get to the next stage of actually being interviewed for a job by a real person. AI approves who you should interview
I soon get an encouraging email from Michael Morris, chief executive of Employsure – a company which provides advice on workplace relations and health and safety issues to small businesses. If I ever give up journalism, Morris tells me, I can try for a new career at Employsure. AI has approved me. Despite my deep scepticism about the process, I can’t help but feel a little pleased by the bot’s assessment.
That is because my rather self-serving answers to random personality questions fit those of the best performers at Employsure. There’s no possibility of ageism or sexism or any other latest “ism” influencing that. No old schoolmates or university or sporting framework, no biases about looks or clothes or mannerisms or personal history.
Instead, I participated in what is a variation on a personality test – based on the algorithmic analysis provided by another company, Sapia, operating in Europe and Australia and with 20 clients.
Morris says Employsure tested the performance of employees selected by Sapia’s algorithm against the choices of Employsure’s own human recruitment team for much of last year.
The fast-growing company hired around 450 people in 2018 with a workforce now totalling more than 800. Morris wanted good people and those more likely to stay.
The experience convinced him that rather than using more traditional CVs to screen applicants, it was worth paying Sapia for its AI technology as Employsure continues to expand its numbers this year. Employsure now only interviews the 10-15 per cent of those who are graded “yes” or “maybe” by the bot.
“The overlay of AI made a significant difference in overall performance, productivity and tenure,” Morris says. “And it means the recruitment team can have a head start on engaging in better conversations with those who have interviews.” This is still a distinct minority view among Australian businesses which have been generally reluctant to embrace the promise of AI when it comes to hiring.
Read: The Ultimate Guide to Interview Automation
The trend to make greater use of AI in business generally is inevitable and accelerating. Just consider all those online “conversations” we now have about customer service and products as the ever-patient bot nudges us this way and that.
Just as inevitably, it is leading to community concerns about whether AI will be used to replace too many people’s jobs. According to a study by the McKinsey Global Institute, intelligent agents and robots could eliminate as much as 30 per cent of human labour by 2030. The scale would dwarf the move away from agricultural labour during the 1900s in the United States and Europe.
Of course, the record of technology shifts over centuries always ending up creating many more other types of jobs does not completely soothe fears that this time it’s different. Even if such alarm is overstated, dramatic changes in technology can certainly prove socially and economically disruptive for long periods. AI can also be scary.
But this version of AI is more about filling new jobs more efficiently. Many large global companies already use it to filter job applicants, especially those coming in at lower levels. Its advocates argue it efficiently eliminates bias or the tendency for people to hire in their own image.
Not that this always goes smoothly – even for the most digitally sophisticated businesses. Amazon abandoned its own AI hiring tool last October when management realised it had only introduced more bias into the process. Its AI system was based on modelling the CVs of those already at the company – who tended to be male. Naturally, that made prospective hires more likely to be male too. So much for gender-diversity targets.
Sapia’s chief executive is Barb Hyman, formerly a human resources executive for the online real estate advertising company REA Group. She says the system doesn’t work for those companies that don’t measure the performance of their existing employees but the data becomes more and more accurate as more information is added.
By matching responses of applicants against only those employees who are already doing well, it can be extremely efficient with immediate payback – especially for larger companies. The data can also be used to change the culture in an organisation by screening the types of personalities who are hired.
Not surprisingly, Hyman says the data demonstrates how different personalities are better fitted to different sorts of roles. So those who do well in caring jobs tend to be reliable and demonstrate traits of modesty and humility. Good salespeople are focused, somewhat self-absorbed, disorganised and transactional. Those who are involved in building long-term business relationships need to be more adaptable, resilient and open.
Sounds more like common sense than AI. But there’s less and less of that around anywhere. AI beckons instead.