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 .
https://medium.com/mit-media-lab/the-algorithms-arent-biased-we-are-a691f5f6f6f2
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.
Related
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Joe & the Juice Partners with Sapia.ai, Scaling an Exceptional Candidate Experience and Cutting Time to Hire
Read the full press release about the partnership here.
Joe & the Juice, the trailblazing global juice bar and coffee concept, is renowned for its vibrant culture and commitment to cultivating talent. With humble roots from one store in Copenhagen, now with a presence in 17 markets, Joe & The Juice has built a culture that fosters growth and celebrates individuality.
But, as their footprint expands, so does the challenge of finding and hiring the right talent to embody their unique culture. With over 300,000 applications annually, the traditional hiring process using CVs was falling short – leaving candidates waiting and creating inefficiencies for the recruitment team. To address this, Joe & The Juice turned to Sapia.ai, a pioneer in ethical AI hiring solutions.
A Fresh Approach to Hiring
Through this partnership, Joe & The Juice has transformed its hiring process into an inclusive, efficient, and brand-aligned experience. Instead of faceless CVs, candidates now engage in an innovative chat-based interview that reflects the brand’s energy and ethos. Available in multiple languages, the AI-driven interview screens for alignment with the “Juicer DNA” and the brand’s core values, ensuring that every candidate feels seen and valued.
Candidates receive an engaging and fair interview experience as well as personality insights and coaching tips as part of their journey. In fact, 93% of candidates have found these insights useful, helping to deliver a world-class experience to candidates who are also potential guests of the brand.
“Every candidate interaction reflects our brand,” Sebastian Jeppesen, Global Head of Recruitment, shared. “Sapia.ai makes our recruitment process fair, enriching, and culture-driven.”
Results That Matter
For Joe & The Juice, the collaboration has yielded impressive results:
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33% Reduction in Screening Time: Pre-vetted shortlists from Sapia.ai’s platform ensure that recruiters can focus on top candidates, getting them behind the bar faster.
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Improved Candidate Satisfaction: With a 9/10 satisfaction score from over 55,000 interviews, candidates appreciate the fairness and transparency of the process.
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Bias-Free Hiring: By eliminating CVs and integrating blind AI that prioritizes fairness, Joe & The Juice ensures their hiring reflects the diverse communities they serve.
Frederik Rosenstand, Group Director of People & Development at Joe & The Juice, highlighted the transformative impact: “Our juicers are our future leaders, so using ethical AI to find the people who belong at Joe is critical to our long-term success. And now we do that with a fair, unbiased experience that aligns directly with our brand.”
Trailblazing for the hospitality industry
In an industry so wholly centred on people, Joe & the Juice is paving the way for similar brands to adopt technology that enables inclusive, human-first experiences that can reflect a brand’s core values.
If you’re curious about how Sapia.ai can transform your hiring process, check out our full case study on Joe & The Juice here.
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Sapia.ai Wrapped 2024
It’s been a year of Big Moves at Sapia.ai. From welcoming groundbreaking brands to achieving incredible milestones in our product innovation and scale, we’re pushing the boundaries of what’s possible in hiring.
And we’re just getting started 🚀
Take a look at the highlights of 2024
All-in-one hiring platform
This year, with the addition of Live Interview, we’re proud to say our platform now covers screening, assessing and scheduling.
It’s an all-in-one volume hiring platform that enables our customers to deliver a world-leading experience from application through to offer.
Supercharging hiring efficiency
Every 15 seconds, a candidate is interviewed with Sapia.ai.
This year, we’ve saved hiring managers and recruiters hours of precious time that can now be used for higher-value tasks.
Giving candidates the best experience
Our platform allows candidates to be their best selves, so our customers can find the people that truly belong with them. They’re proud to use a technology that’s changing hiring, for good.
Leading the way in AI for hiring
We’ve continued to push the boundaries in leveraging ethical AI for hiring, with new products on the way for Coaching, Internal Mobility & Interview Builders.
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Situational Judgement Tests vs. AI Chat Interviews: A Modern Perspective on Candidate Assessment
Choosing the right tool for assessing candidates can be challenging. For years, situational judgement tests (SJTs) have been a common choice for evaluating behaviour and decision-making skills. However, they come with limitations that can make the hiring process less effective and less inclusive.
AI-enabled chat-based interviews, such as Sapia.ai, provide organisations with a modern alternative. They focus on understanding candidates as individuals and creating a hiring experience that is both fair and insightful while enabling efficient screening and selection.
This shift raises important questions: Are SJTs still a tool that should be considered for volume hiring? And what do AI assessments offer in comparison?
1. The Static Nature of SJTs
Traditional SJTs use predefined multiple-choice questions to assess behavioural tendencies and situational knowledge. While useful for screening, these static frameworks lack the flexibility to adapt based on real-world performance data or evolving role requirements.
Once created, SJTs don’t adapt to new data or evolving organisational needs. They rely on fixed scenarios and responses that may not fully reflect the dynamic realities of modern workplaces, and as a result, their relevance may diminish over time.
AI-enabled chat interviews, on the other hand, are inherently adaptive. Using machine learning, these tools can continuously refine their models based on feedback from real-world outcomes such as hiring or turnover data. This ability to evolve ensures the assessments align with organisations’ needs.
2. Richer Data Through Open-Ended Responses
One of the main critiques of SJTs is their reliance on multiple-choice responses. While structured and straightforward, these options may not capture the full scope of a candidate’s thinking, communication skills, or problem-solving ability. The approach is often limiting, reducing complex human behaviour to a few predefined choices.
AI-enabled chat interviews work more holistically and dynamically. These tools provide a more complete picture of a person by allowing candidates to answer questions in their own words. Natural language processing (NLP) analyses their responses, offering insights into personality traits, communication skills, and behavioural tendencies. This open-ended format lets candidates express themselves authentically, giving employers a deeper understanding of their potential.
3. The Candidate Experience: Stressful or Supportive?
SJTs often include time constraints and rigid formats, which can create pressure for candidates. This is especially true when candidates feel forced to choose options that don’t fully reflect how they would actually behave. The process can feel impersonal, even transactional.
In contrast, chat-based interviews are designed to be conversational and low-pressure for candidates. By removing time limits and adopting a familiar chat interface, these tools help candidates feel more at ease. They also frequently include personalised feedback, turning the assessment into a valuable experience for the candidate, not just the employer.
4. Addressing Bias and Fairness
Traditional SJTs are prone to transparency issues, as candidates can often identify and select the “best practice” answers without revealing their true tendencies. Additionally, static test designs can unintentionally embed bias; due to the nature of the timed test, SJTs have been found to disadvantage some groups.
AI chat interviews, when developed ethically within a framework like Sapia.ai’s FAIR Hiring Framework, eliminate explicit bias by relying solely on the content of a candidate’s responses. Their machine learning models are continuously validated for fairness, ensuring that hiring decisions are free from subjective judgments or irrelevant demographic factors.
5. An Assessment That Improves Over Time
Workplaces are constantly changing, and hiring tools need to keep up. SJTs’ fixed nature can make them less effective as roles evolve or organizational priorities shift. They provide a snapshot but not a dynamic view of what’s needed.
AI-enabled chat interviews are built to adapt. With feedback loops and continuous learning, they incorporate real-world hiring outcomes—like retention and performance data—into their models. This ensures that assessments stay relevant and effective over time.
Rethinking Candidate Assessment
As hiring demands grow more complex, so does the need for tools that can capture the whole person, not just their response to hypothetical scenarios. While SJTs have played an important role in hiring practices, they are increasingly being replaced by tools like AI-enabled chat interviews.
These modern approaches provide richer data, adapt to changing needs, and create a richer and more engaging experience for candidates. Perhaps most importantly, they emphasise fairness and inclusivity, aligning with the growing demand for unbiased hiring practices.
For organisations evaluating their assessment tools, the question isn’t just which method is “better.” Understanding the specific needs of your roles, teams, and candidates will help you choose tools that help you make decisions that are both informed and equitable.