This came up in my feed last week prompting me to share my own 2 cents on why machines are better at hiring decisions than humans.
Did you know that the Wikipedia list of cognitive biases contains 185 entries? This somewhat exhausting article lays out in excruciating detail biases I didn’t know could exist and arrives at the conclusion that they are mostly unalterable and fixed regardless of how much unconscious bias training you attend in your lifetime.
I get asked A LOT about how I can work for a company that sells technology that relies on ‘machines’ to make people decisions.
I will keep it simple … 2 reasons
Because as per above, our biases are so embedded and invisible mostly we just can’t check ourselves in the moment to manage those biases. (I would rather hire women, ideally, mums, who like the same podcast series as me and straight through to offer stage if they like Larry David humour )
And Machines can be ‘trained’ …humans can’t, as easily or efficiently
But the myriad and ever-present news articles about ‘algorithmic bias’ has lumped all machine learning into one massive alphabet soup of ‘don’t trust the machine!
Really? Are we also biased against machines now? I saw Terminator 2 as well and worry about machines taking over the world ….but that’s a massive leap from the practice of bringing data, objective data into the most critical decision you will make as a people leader, who to hire. The divorce rate is for me the proof point that humans suck at making critical people decisions.
I’ve been in the People space for a while. I was lucky enough to work with 2 organisations BCG and the REA Group that value their people above all else. They also value making money and having your engineers and consultants sucked up in recruiting days and campaigns is a massive investment of your scarce and valuable capacity. I have found most companies don’t even know how much it costs to hire one person because no one is tracking the time investment.
We are all time poor and so we often default on hiring based on ‘pedigree’ . Someone has GE on their CV, they must be great as GE only hires great people. That’s a pretty loose /random data point for making a hiring decision
So here is a non data scientist view of why you should trust machine learning to find the right people and when you shouldn’t
First credit to this post which helped me put this into non tech speak .
Why use Machine Learning at all for decision-making ? Because it underwrites making repeatable, objectively valid (ie data based) decisions at scale.
Value to the organisation:
• Use less resources to hire
• Every applicant gets a fair go at the role
• Every applicant is interviewed
• Hire the person who will succeed vs someone your gut tells you will succeed
How do you ensure there is no or limited bias in the machine learning ?
Take a look at:
– what’s the data being used to build the model
– what are you doing to that data to build the model
If you build models off the profile of your own talent and that talent is homogenous and monochromatic, then so will be the data model and you are back to self reinforcing hiring
If you are using data which looks at age, gender, ethnicity and all those visible markers of bias , then sure enough, you will amplify that bias in your machine learning
Relying on internal performance data to make people decisions, that’s like layering bias upon bias. The same as building a sentencing algorithm with sentencing data from the US court system, which is already biased against black men.
Reality is that machine learning is by its very definition aiming to bias decisions, and removing bias is driven by what bits of training data you use to feed the machine. This means you can make sure the data you train with has no bias.
Machine learning outcomes are testable and corrective measures remain consistent, unlike in humans. The ability to test both training data and outcome data, continuously, allows you to detect and correct the slightest bias if it ever occurs.
Tick to objective data which has no bio data (that means a big NO to CV and social media scraping )
Tick to using multiple machine learning models to continuously triangulate the model vs rely on one version of truth
So instead of lumping all AI and ML into one big bucket of ‘bias’ , look beneath the surface to really understand what’s going into the machine as that’s where amplification risks looms large
Oh and the reason why I hate Simon Sinek …
I don’t actually at all, but if a candidate said that to me in an interview I’d probably hire them for it because I would make some superficial extrapolation about their personality based on it:-
• first it would tell me they watch ted talks and so that eeks of cleverness and learning appetite
• second it would tell me they are confident to be contrarian and that would make me believe that they are better leaders
• third I would infer they are not sucked into the vortex of thinking that culture is the panacea to every people problem.
See how easy it is to make an unbiased hiring decision?
Soon (maybe already) you will be putting yours and your loved ones lives in the hands of algorithms when you ride in that self driven car. Algorithms are extensions to our cognitive ability helping us make better decisions, faster and consistently based on data. Even in hiring.
Being able to access interview automation just got so much easier inside Tribepad, with Sapia. To explore the use cases for Sapia, let’s chat.
Here’s a quick rundown:
And now that we are integrated into Tribepad, you get all of these smarts inside your existing Tribepad application. At Sapia, we interview every applicant in-depth and at scale for you. Overall, this is by using a text chat that helps you find the best people fast. Our underlying data science has been accepted and published in international journals.
Firstly, no one’s time is served well by screening thousands of CVs. With every additional applicant costs your business an extra $20 in screening if you are doing it the old way, automating the screening process is the commercial decision companies are now making.
Once your vacancy is created in Tribepad, a corresponding interview link will also be created.
Candidates click this link to enter their text-based interview. This is known as the ChatInterview.
As soon as candidates complete their interview their results are displayed inside Tribepad. You also get to see the candidate’s personality assessment. With the pre-assessment already done for you, it makes shortlisting much faster. Thus, by sending out one simple interview link, you nail speed, quality and candidate experience.
The SmartInterview experience is most commonly used for high-volume recruiting. Our customers typically use it in frontline customer-facing roles (like contact centres, customer service) and/or for low-skill roles.
We help manage the disconnect between attraction and retention. This is all done by allowing Recruitment Teams to work more efficiently to hire the best talent. All is done whilst ensuring the applicants feel good about applying for a job role.
Sapia solves the time problem of managing a large applicant pool. It also tackles the quality problem of pin-pointing the best people from that pool. Additionally it solves the candidate experience problem by offering every applicant a fair chance at the opportunity (everyone gets an interview) on platforms they love to use. Simultaneously every candidate gets something of immense value in return for their application.
We are glad you are asked! The first thing to note is Sapia is a paid app and sold separately. Next, to explore the pricing that suits your organisation, let’s chat. Lastly, our team can take you through the integration process and describe how the interview automation experience works.
Also, to keep up to date on all things “Hiring with Ai” subscribe to our blog!
It’s not every day or every job where you get to say you are changing the way the world works.
For 2+ years, the small team of incredibly dedicated data scientists led by the incredibly humble Buddhi Jayatilleke have tested and re-tested and experimented and re-experimented to find a new formula for assessing talent – one that is 100% inclusive and bias-free, but also human, using the combination of AI, machine learning and advances in NLP.
Apart from reading daily the thousands of comments of gratitude we receive from candidates for this new formula, which is globally unique! it is wonderful to see that team receive the industry acknowledgement at a global level.
Last week, at a Virtual CogX, the world’s largest Festival of AI and Emerging Technology, with top CEOs, Scientists, Technologists, Data Scientists in attendance, with over 30,000+ attendees from hundreds of countries, 6500 world leaders and 650 presenters across 17 forums, this team were awarded Top 3 For Best AI in HR technology.
For a team that has been tackling this problem for such a small amount of time, with limited resources but endless tenacity and commitment, we couldn’t be prouder to get to work with them every day.
The PredictiveHire Data Science Team:
Are you interested in using an award-winning solution in your business to recruit faster, better, fairer? Let’s chat
To find out how to use Recruitment Automation to ‘hire with heart’, we also have a great eBook on recruitment automation with humanity.
Most people are very familiar with a performance review. It’s the annual anxiety fest when every employee has their performance assessed and rated, perhaps against benchmarks agreed at last year’s review or defined by their job description.
So is a talent review basically the same thing? Well yes and no. While a talent review will still see employees rated and ranked, the focus extends beyond current and recent performance to consider their potential as future leaders in senior or key roles within the business. It’s all about mapping an organisation’s business needs against the capabilities and potential of its people.
Talent review plays an essential role in business planning, pinpointing skill gaps and helping organisations to develop and retain their best talent.
Forward-thinking organisations believe that talent review is bigger than an annual event. Rather, it’s an essential part of an always-on process of talent management that fosters a high-performance culture from the very first engagement with employees.
Sapia’s Ai-enabled chat interview platform helps businesses to plan for future success by ensuring candidates with the very best potential are identified and engaged upfront. This approach provides talent momentum from the outset, ensuring every hire is building ‘bench strength’ and providing leaders with confidence that the next generation is ready to step-up and step-into key roles as needed.
It’s no secret that high performers and team leaders share certain personality traits and behaviours. In fact, it’s a science that organisations have long embraced in their pursuit of excellence and competitive advantage.
Since it was first published in 1962, The Myers-Briggs Type Indicator that classified 16 personality types has been at the heart of most personality assessments and recruitment science. Much of the appeal of Myers-Briggs was its simplicity in reducing complexity to concise descriptors. These descriptors may have sufficed when only human intelligence was doing the processing and decision-making.
But in an age of data, it’s a big compromise – a compromise in accuracy, nuance, and the real diversity of personality types that exist in our population. It’s also a compromise we no longer need to make.
Read: Hire for Values
Sapia is a leading innovator and advocate of leveraging data and technology to enhance the recruitment process. In developing our award-winning automated chat interview platform, our data science team looked at how we could move beyond the limits of Myers-Briggs personality testing.
Our data team fed text responses to interview questions from 85,000 job applicants into our personality classifier. Spread across two regions, the UK and Australia, 47% of applicants were identified as male, 53% as female.
Identifying 400 unique personality groupings and how they could be usefully applied to decision-making is beyond the ability of the human brain… but not beyond technology. Using Natural Language Processing (NLP) and machine learning, our artificial-intelligence enabled platform got to work with findings that were both surprising and not surprising at all.
What did we find?
The ‘not surprising’ part of our research is that even at 400 groupings, there are distinct differences in personality profiles. It’s not surprising when you consider that humans are not linear beings and that our personalities are highly complex and nuanced.
The most surprising thing we discovered was that personality types by role were distinct. The personality profiles attracted to sales roles, for example, were noticeably different from the profiles attached to a carer role. Even more surprising were the imperceptible differences in the personality distribution across the 400 types between men and women – a sign of how conscious or unconscious biases can play into our decision processes.
Differentiated by size, sector, structure and history, every organisation is unique. So every talent review will be unique too. Talent reviews need to be designed around the specific needs of the business but generally will bring performance management, learning and development and succession planning together.
When senior leaders meet for a talent review, their principle objective is to talk about the performance of individual employees in their teams and how those employees might take on more responsible roles in the future. Through this process, the critical positions in an organisation will be identified. Critical positions mean any role that business operations would stop or be seriously compromised if no one was able to step into the role immediately.
Keep in mind that these critical roles may not necessarily be management roles and will also depend on the nature of the business. In a manufacturing business, for example, the chief engineer might be solely responsible for keeping a production line in working order. Talent reviews need to consider every employee across an organisation.
An ongoing talent review process not only matches an organisation’s talent to existing roles, but it also helps identify new roles that will need to be created to achieve plans for future growth or expansion. It’s also possible that as a company moves forward, key roles may change or even become redundant. The most successful businesses are dynamic and flexible.
A structured review process reviews employees in terms of key strengths, career ambitions and readiness for promotion. Talent reviews provide a forum for a range of important conversations that every organisation interested in best practice needs to have:
There is a range of methods that organisations use to assess their employees for talent reviews. While some will arrive at a ranking or score, others may use a more nuanced approach to assessing their talent.
Talent reviews can often reveal glaring disparity and bias in team leaders’ expectations of employees and how they rate them. An agreed and standardised approach across the organisation is essential. By ensuring employee expectations are aligned among leaders and cultural values are socialised across the organisation, potential friction around accountability can be diffused.
Rank and yank – what not to do
Though their ranking process has long been dropped, Jack Welch, the celebrated or controversial (pick your own path!) CEO of General Electric once insisted on an evaluation that reduced every employee’s performance to a number. Following evaluations each year, the lowest ranking 10% were fired across the business. In contemporary business, this ‘rank and yank’ approach would not be considered best-practice HR.
The 9-box performance and potential matrix
A less controversial ranking for employees is the 9-box matrix. This commonly-used assessment tool assigns employees to one of nine boxes on a grid that on one axis rates their performance (underperformance, effective performance, outstanding performance) and on the other rates their potential (low, medium, high). Employees ranked in the box where outstanding performance and high potential meet are those assessed most likely to be future leaders.
Taking a step back from the talent review process, Sapia has worked to solve and improve the frontier problem of every recruiter and every employer – how to get the right talent on board sooner.
With policies and process to put the best candidates in place every time, ongoing talent management and talent reviews can be more streamlined and rewarding for employers and employees alike.
The first step to creating a step-change in the process is ensuring that everyone is assessing talent on the same criteria. These need to align with your organisation’s specific needs and values, which are ideally defined and documented as part of your business, brand and employer brand plans.
While Sapia’s early data breakthroughs were based on 85,000 interview responses, machine learning and artificial intelligence means that our platform never stops learning. Today, our Ai-powered platform has analysed more than 165 million words in text-based interviews from more than 700,000 candidates.
Continuous learning means that Sapia can help recruiters and employers make smarter, evidence-based employment decisions at the early career stage.
Within our science-based approach, behavioural interview questions are tailored around the agreed assessment criteria for the role. These questions are related to past behaviour to reliably assess personality traits. They can be customised to the specific role family – sales, retail, customer service etc– and aligned to the organisation’s agreed values and characteristics that will define their leaders of tomorrow.
Sapia’s bespoke Ai-platform analyses candidates’ responses across a range of criteria including readability, text structure, semantic alignment, sentiment and personality to identify candidates with the best future potential.
Making the wrong choices for future leaders can put your business at risk. At times of talent review, careers can be derailed and employees demotivated. A properly executed talent management process that begins with smarter recruitment choices is one of the best investments in the future of your business.
The insights delivered through a disciplined, standardised and ongoing process of talent assessment can be used at both organisational and managerial levels to drive your business forward. Creating a culture of high performance begins with best practice in early career candidate assessment. With Sapia’s platform as a key element, a robust talent review and management process will work to:
This article is presented by Sapia as part of our mission to promote best practice in contemporary recruiting and HR. Our Ai-enabled text chat interview platform can help any organisation identify future leaders while providing candidates with an efficient, empowering and enjoyable experience. The user satisfaction rate for our award-winning platform is 99%.