ARTICLE #23:
Kai-Fu Lee: AI Superpowers – China and Silicon Valley | Artificial Intelligence (AI) Podcast
Some really valuable insights in how AI is approached in the Sillicon Valley and China. Recommended because it’s always enlightening listening to Kai-Fu speak.
Back 
Written by Nathan Hewitt
Blind screening – the smarter alternative to situational judgement tests?
To find out how to use Recruitment Automation to ‘hire with heart’, we also have a great eBook on recruitment automation with humanity.
Situational Judgement Tests (SJTs) or Ai interview automation?
From one recruiter to another and one employer to another, the ways candidates are selected vary greatly. But ask anyone involved in the process, and most will agree that what happens at the early candidate screening stage, is critical to getting the best outcomes. Traditionally, it’s also been the most time-consuming and costly part of the hiring process.
Long before a face-to-face interview, recruiters need to screen candidates to decide, from potentially thousands of applicants, who should proceed to the next steps in the hiring cycle.
But before they’ve even met a candidate, can recruiters really assess someone’s ability and suitability for the job they’re applying for? Yes, they can.
Choosing the best assessment solution for your recruitment tools suite.
In contemporary recruiting, a suite of tools and technologies can help take the hard work and the guesswork out of the hiring process. Talent assessment tools help recruiters identify the best candidates faster – talent who will be the best fit for the role and the team, work most productively and stay in the role longer.
While traditionally a time-consuming manual review of applications and CVs would begin the hiring process, recruiters have embraced technologies that can automate these processes from the outset.
In this article, we compare two top of the funnel tools recruiters are using to assess candidates: traditional situational judgement tests (SJTs) and the next generation text interview platform.
Sapia Ai-enabled automated interviews could provide the answers you’re looking for, helping to connect to the best talent faster and more cost-effectively.
So, what is a situational judgement test?
Situational judgement tests are used to assess a candidate’s judgement and ability to respond appropriately to the real-world situations they would be likely to encounter in the workplace.
Candidates are presented with a workplace scenario and then they are required to choose or rank the best (or worst) paths to resolve the challenge, conflict or opportunity. They are a type of psychological aptitude test that provides insight and assessment of a candidate’s job-related skills.
What do situational judgement tests measure? 
While the challenging scenarios presented to candidates are hypothetical, the best tests are designed around the role they are applying for.
Reflecting real situations they could encounter, the scenarios may involve working with other team members or supervisors, interacting with customers or dealing with day-to-day challenges.
Situational judgement tests date back to the 1940s. While the ways they are delivered may have changed, they remain a popular way to assess skills such as problem-solving and interpersonal skills. They are also useful in assessing soft skills and practical, non-academic intelligence.
Situational judgement tests are customised to the role and the organisation. Generally, they would be looking to assess a candidate’s aptitude for a role by measuring competencies that might include:
- Communication skills – clarity, persuasiveness, empathy
- Organisation and planning – solving the problem, staying cool under pressure
- Teamwork – collaboration, encouraging others, prioritising team needs over the individual, implementing solutions
- Decision making – exercising discretion, analysing the situation, demonstrating solid judgment
- Customer focus – listening, recognising, delivering
- Initiative – taking responsibility, demonstrating leadership, stepping up
- Ambition – drive to achieve
How does a situational judgement test work?
As they are produced by a range of different providers, SJTs can be delivered in a number of ways. As they are also tailored to suit specific roles and companies, tests can vary in their length, structure and format. While some may be paper-based ,most tests are delivered digitally.
The tests provide candidates with a workplace scenario – as a written description or as a video or digital animation – and a challenge related to that scenario. Typically, candidates are then presented with four or five possible paths of action in multiple-choice format to deal with the situation described.
Different approaches are used for candidates to provide their answers. Some may require candidates to choose both the most desirable and the least desirable action. Others may ask candidates to choose just one preferred option or rank all actions in terms of effectiveness.
What benefits can SJTs bring to the hiring process?
SJTs are typically used before the interview stage and often used in combination with a knowledge-based test.
SJTs are designed to help recruiters and hiring managers to:
- filter candidates from large talent pools
- identify candidates who are likely to perform best in the role
- provide candidates with further insight into the demands of the role
- identify candidates who will be a good cultural fit
- assess candidates’ aptitude and judgement against realities of the role
- understand a candidate’s aptitude for the particular job
- help reduce staff turnover by making more informed decisions
Sapia – the smarter way to assess candidates
Since 2013, Australian recruitment technology specialist Sapia has worked to solve a problem for every recruiter and employer. That is how to get to the right talent faster while consistently improving the candidate experience.
Sapia’s text-based interview platform uses artificial intelligence, machine learning and natural language processing to provide reliable personality insights into every candidate. While SJTs can be expensive time-consuming to create, administer and assess, Sapia’s platform can a like-for-like personality and job-fitness tests with far greater ease and at a fraction of the cost.
Why organisations are turning to interview automation over situational judgement tests
Here is feedback from a customer after running a pilot using SJTs:
Often SJTs don’t accurately represent what the job is really about. There are so many aspects that need to be considered within a real-world situation. Feedback from the SJTs pilot groups is that they often felt as though they were being forced into specific areas that may not be job-related. There needs to be more flexibility for a candidate to say: “I would do this, but I would also do a bit of that”. Having an experience that gives flexibility in answering. It enables candidates to have that open-ended answer to express what was important to them.
How Sapia’s interview automation works
Smart Interviewer is Sapia’s machine learning interview platform. With learning from analysing more than 165 million words in text-based interviews with more than 700,000 candidates, Smart Interviewer combines standard interview questions related to past behaviour and situational judgement to reliably assess personality traits. The questions can be customised to the specific role family – sales, retail, call centre, service etc– and specific requirements relating to the employer’s brand and employment values.
Candidate assessment at scale
The scientific foundation of Sapia’s Ai interview platform is that language forms the framework for the knowledge, skills and personality we possess. Through a simple text-based conversation, Smart Interviewer provides valuable candidate insights. It can predict a candidate’s suitability for a role and guide their progression through the recruitment process. It delivers the insights that recruiters and employers need to make better hiring decisions at scale.
Enhancing the candidate experience – 99% satisfaction
Improving the candidate experience is a priority for every recruiter and employer. The effect of a poor experience can cause lasting damage to reputations and brands. Sapia is the only conversational interview platform with 99% candidate satisfaction feedback. Candidates enjoy the process, appreciate the opportunity and value the personalised feedback. Something that’s simply not practical with most high-volume recruitment briefs.
Candidates know text and trust text
As text is a familiar, non-confrontational way to connect, candidates enjoy the text interview experience. Unlike SJTs that lock them into choosing options from pre-determined answers, candidates appreciate the open-ended questions . Here they are empowered by the opportunity to tell their story in their words.
While questions are customised to the role, some typical examples include:
• What motivates you? What are you passionate about?
• Not everyone agrees all the time. Have you had a peer, teammate or friend disagree with you? What did you do?
• Give an example of a time you have gone over and above to achieve something. Why was it important for you to achieve this?
• Sometimes things don’t always go to plan. Describe a time when you failed to meet a deadline or personal commitment. What did you do? How did that make you feel?
• In sales, thinking fast is critical. What qualifies you for this? Provide an example.
Tackling bias and taking CVs out of the equation
Sapia provides blind-screening at its best, effectively reducing opportunities for bias from the assessment process to ensure every candidate is playing on a level field. Candidates recognise and appreciate the opportunity to tell their story without the subjective biases of a human interview or a cursory review of their CV. For top of the recruitment funnel interviews, Sapia removes CVs from the process altogether.
Find out more about Sapia’s Ai-powered candidate assessment tool and how it could replace your time-consuming and costly SJTs today.
You can leave us your details to get a personalised demo OR try out Sapia’s Chat Interview right now, here.
Related
Blog
Sapia is named Candidate Experience Solution of the Year
Frontier technology that puts people at the heart of their recruitment solution is rewarded for its ground-breaking approach that also solves for bias and reduces recruitment costs.
Melbourne, Australia, September 21 – Sapia, a text-based AI recruitment solution has been recognised globally for its commitment to creating a hiring experience that is empowering and motivating to the individual and which enhances your company’s brand.
The TIARA Talent Tech Star, awarded to Sapia, honours the most exemplary businesses globally in the talent acquisition industry.
Selected from a group of international finalists as a bold and innovative startup, Sapia was deemed top of the class. All for having demonstrated the value and impact of their solution at a time when agency and in-house recruiters are embracing technology and new ways of working.
The Candidate Experience Solution of the Year Award recognised Sapia as
“a matching solution that could fundamentally change the way
candidates experience recruitment, delivering valuable insight to both the
employer and the candidate whether they are recruited or not.”
The judges included the Head of Search & Staffing UK&I and EMEA at LinkedIn, Sales and Marketing Director of ManpowerGroup UK, and Head of Innovation and Transformation at PwC.
Sapia awarded for solving a core pain point
Sapia CEO Barb Hyman said the team was honoured to receive industry recognition for their candidate experience solution which was a core pain point that the company solved along with bias and recruitment costs.
“For far too long as an industry HR has failed so many jobseekers by not giving everyone an equal chance to prove themselves and then ghosting those who do make it through to interviews,” Hyman said.
“With Sapia, everyone gets a chat interview and is treated equally by replying to text-based questions on their phone, without any demographic data being used to make hiring decisions.”
She said the company’s goal around candidate experience is to be recognised as the most inclusive recruitment solution at scale. The team has done extensive testing on how different groups respond to chat-based interviews. This includes a range of candidates from a multitude of gender, race and language backgrounds. For these groups, the experience has been transformative. For candidates who might otherwise feel intimidated by a video format feel safe and comfortable interviewing by chat. The demographics collected on this front are only used in reporting for HR leaders against DEI targets, and not in any hiring decisions.
“We hold ourselves to incredibly high standards when it comes to creating an inclusive product, and ultimately it’s placing people at the core of what we do, that sets us apart from others, and makes our solution so successful for our customers.”
The Qantas Group, the Iceland Group, Telefonica, Bunnings and other trusted consumer brands have seen dramatic improvements from applying Sapia to their hiring/ promotion decisions.
About Sapia
Sapia (Formerly PredictiveHire) is a frontier technology solution which solves for three pain points in recruiting: bias; candidate experience, and efficiency. With only five free-text behavioural questions taking around 20 mins, and using over 80 features extracted from the candidate responses, our predictive models assign a “suitability” score to each candidate. To date, 400,000 candidates looking for roles in retail, healthcare, customer service, hospitality, contact centres and graduate roles across 34 countries have experienced Sapia with positive feedback averaging 99%.
Media Contact | Barb Hyman, CEO barb@sapia.ai
Have you seen the 2020 Candidate Experience Playbook?
Finally, if there was ever a time for our profession to show humanity for the thousands that are looking for work, that time is now.
Blog
AI Tech Company in Melbourne – What inspires us!
‘What engages us’ is curated by the PredictiveHire team, a team of pioneers working at the frontier of 3 huge trends:
1. AI in HR, especially people selection. Because who you hire and who you promote are the most critical business decisions you make across most roles and organisations.
2. Soft skills are now the real skills that matter and until now, very hard to assess accurately, unbiased and efficiently.
3. Advances in computational linguistics + processing power mean we can DNA personality from the text in a few seconds.
We are the only AI solution in the world that uses the convenience of an interview via text to screen talent. At the same time, we also give deep personalised insights to every applicant who completes the interview, and every hiring manager using our solution. The absence of any subjective information in our AI data collection also means our assessment is without bias. At last technology that truly does level the playing field.
Being pioneers we consume new ideas and research on a range of topics in our field because we are all learners in this space. Here we share what we are discovering, listening to, watching and reading … We hope you find these shares as useful and inspiring as we do!
OUR FAVOURITE BOOKS! 
Ethical Algorithm
Michael Kearns and Aaron Roth
Why we love it! Because it challenges every organisation using Ai to push the boundaries of fairness.
Everybody Lies
Seth Stephens-Davidowitz
Why we love it! Because in everything we do we must always check ourselves for the alternative impacts.
Dataclysm
Christian Rudder
Why we love it! Because in everything we do we must always check ourselves for the alternative impacts.
Civilized to Death
Christopher Ryan
Why we love it! Because this made us think that what we achieve must positive and make everyone feel good!
Prediction Machines
Ajay Agrawal, Joshua Gans, Avi Goldfarb
Why we love it! Because this was the first book on predictive analytics read by our CEO Barb which helped a lot to explain this space using simple concepts. 
How Smart Machines Think
Sean Gerrish
Why we love it! Because this was recommended by Matt, one of our awesome advisors.
Invisible Women: Data bias in a world designed for Men
Caroline Criado Perez
Why we love it! Whilst the audio version does feel a bit didactic at times, the narrator is so frustrated at the disconnect between the facts and what people believe about the presence or not of bias. There is some solid data referenced which reflects the deep and wide research that has gone into uncovering often invisible nature of gender bias in many sectors.
NOW FOR OUR FAVOURITE PODCASTS
PODCAST #1
Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning
Michael Kearns is a professor at University of Pennsylvania and a co-author of the new book Ethical Algorithm that is the focus of much of our conversation, including algorithmic fairness, bias, privacy, and ethics in general. But, that is just one of many fields that Michael is a world-class researcher in, some of which we touch on quickly including learning theory or theoretical foundations of machine learning, game theory, algorithmic trading, quantitative finance, computational social science, and more. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai(37 kB)
Why we recommend it? Very informative podcast about AI fairness with Prof Michael Kearns, a co-author of the book Ethical Algorithm.Buddhi is a regular consumer of Lex Fridmans podcasts – he attracts an extraordinary array of minds and perspectives from Daniel Kaheman, Melanie Mitchell, Paul Krugman, Elon Musk and he asks such thoughtful original questions of people interviewed many times over that every podcast feels illuminating for both sides.
PODCAST #2
Scott Adams: Avoiding Loserthink
Dilbert creator and author Scott Adams shares cognitive tools and tricks we can use to think better, expand our perspective, and avoid slumping into “loserthink.”(103 kB)
https://149366099.v2.pressablecdn.com/wp-content/uploads/2019/11/s-adams-500px.jpg
Why we recommend it? There is a story of “bias” in how he got into creating Dilbert. He was told by two employers that “we can’t promote you because you are white, because we have been promoting too many of them, so now we have to fix it”. Essentially Dilbert is a result of him leaving his day job because his employers were trying to fix bias in their promotion process!
PODCAST #3
Getting to scale with artificial intelligence – The McKinsey Podcast
Why we recommend it? Companies adopting AI across the organization are investing as much in people and processes as in technology.
PODCAST #4
Sleepwalkers podcast by iHeartRadio
Why we recommend it? With secret labs and expert guests, Sleepwalkers explores the thrill of the AI revolution hands-on, to see how we can stay in control of our future.
PODCAST #5
HBR IdeaCast: A New Way to Combat Bias at Work on Apple Podcasts
Show HBR IdeaCast, Ep A New Way to Combat Bias at Work – 14 Jan 2020(76 kB
Why we recommend it? A brilliant captivating podcast on the types of biases that turn up at work and an exploration of bias interrupters. Bias and the D & I space is overflowing with content and so it’s inspiring when you come across a wholly original way of labeling it (Bropreating whypeating, and menteruption. What’s less effective -single-bias training … -referral hiring ! because it risks ‘reproducing the demography of your current organisation’ What’s way more effective -correcting the bias in your business systems and the most contrarian view on the topic of performance reviews I’ve read for a while … Keep your performance reviews! Removing them creates a ‘petri dish for bias’.
PODCAST #6
Can Artificial Intelligence Be Smarter Than a Human Being? by Crazy/Genius
Why we recommend it? Surely, AI technology has nothing that even closely resembles human imagination. Or does it? This is a super handy podcast for those who want to know simple ways to explain AI and ML.
PODCAST #7
AI in B2B – a16z Podcast
Why we recommend it? Consumer software may have adopted and incorporated AI ahead of enterprise software, where the data is more proprietary, and the market is a few thousand companies not hundreds of millions of smartphone users. But recently AI has found its way into B2B, and it is rapidly transforming how we work and the software we use, across all industries and organizational functions.
Brilliant articulation of why FOMO is real .. as far as coming to data too late . Co pilot and auto pilot analogy is clever.
1. B2B is different. Companies care a lot about their data
2. Share for greater good and reap the benefits should be the motto of A.I. companies
3. Product design thinking with AutoPilot and CoPilot metaphors. Where can our A.I. be auto and co?
4. Use AB testing to show the benefits to the skeptics
OUR FAVOURITE ARTICLES
ARTICLE #1:
Chief people officer: The worst best job in tech
https://www.protocol.com/worst-best-job-in-tech
Comments: Barb can relate to this one as a former CPO, and whilst the Google case is special, in general, CPO’s should be investing in data driven methods, that allows them to take more informed decisions than not.
ARTICLE #2:
New Illinois employment law signals increased state focus on artificial intelligence in 2020
https://www.technologylawdispatch.com/2020/01/privacy-data-protection/new-illinois-employment-law-signals-increased-state-focus-on-artificial-intelligence-in-2020/
Comments:A read that provoked a bit of discussion amongst the team noting that the Act does not define “artificial intelligence,” a term that is often misunderstood and misapplied even by experts. How will they separate what traditional statistical analysis has been doing to what modern ML algorithms do. Any attempt to classify ML as something different to just statistical analysis at scale will be fun to watch. One can then argue just using averages and medians are a form of AI … Regression .. Correlations … AI bias …
Ask BERT to fill in the missing pronoun in the sentence, “The doctor got into ____ car,” and the A.I. will answer, “his” not “her.” Feed GPT-2 the prompt, “My sister really liked the color of her dress. It was ___” and the only color it is likely to use to complete the thought is “pink.”
ARTICLE #3:
A.I. breakthroughs in natural-language processing are big for business
https://www.google.com/amp/s/fortune.com/2020/01/20/natural-language-processing-business/amp/
Comments:A series of breakthroughs in a branch of A.I. called natural language processing is sparking the rapid development of revolutionary new products.
ARTICLE #4:
Are We Overly Infatuated With Deep Learning?
https://www-forbes-com.cdn.ampproject.org/c/s/www.forbes.com/sites/cognitiveworld/2019/12/26/are-we-overly-infatuated-with-deep-learning/amp/
Comments:Even Geoff Hinton, the “Einstein of deep learning” is starting to rethink core elements of deep learning and its limitations.
ARTICLE #5:
Artificial intelligence will help determine if you get your next job
https://www.vox.com/recode/2019/12/12/20993665/artificial-intelligence-ai-job-screen
Comments:AI is being used to attract applicants and to predict a candidate’s fit for a position. But is it up to the task?
ARTICLE #6:
Biased Algorithms Are Easier to Fix Than Biased People
https://www.nytimes.com/2019/12/06/business/algorithm-bias-fix.html?smid=nytcore-ios-share
Comments:Racial discrimination by algorithms or by people is harmful — but that’s where the similarities end. The study: “Are Emily and Greg more employable than Lakisha and Jamal?” we referenced in our bias paper. We agree with this key point: Algorithmic bias is testable and human bias is not (at least very hard).
ARTICLE #7:
Extroverts Prefer Plains, Introverts Like Mountains
https://bigthink.com/topography-and-personality
Causation or just correlation? There’s a very curious link between topography and personality.
ARTICLE #8:
So what is the difference between AI, ML and Deep Learning?
https://www.linkedin.com/pulse/so-what-difference-between-ai-ml-deep-learning-kanishka-mohaia
ARTICLE #9:
Attractive People Get Unfair Advantages at Work. AI Can Help.
https://hbr.org/2019/10/attractive-people-get-unfair-advantages-at-work-ai-can-help
Algorithms can make sure decisions are about performance rather than looks.
ARTICLE #10:
Artificial Intelligence in HR: a No-brainer
https://www.academia.edu/37977384/Artificial_intelligence_in_hr_a_no_brainer
This is an article from PwC that summarizes the case for AI in HR well. A really good overview.
ARTICLE #11:
Science Behind the IBM’s Personality Service
https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-science
The background and the approach listed here is applicable to our approach too. The difference being, IBM built their models using twitter data whereas ours is more specialised/accurate for recruitment (i.e. based on more data and continuously learning). In addition, we are able to predict more than personality (e.g. job hopping attitude, traits etc).
ARTICLE #12:
Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text
https://www.aaai.org/Papers/JAIR/Vol30/JAIR-3012.pdf
ARTICLE #13:
Language-based personality: a new approach to personality in a digital world
ARTICLE #14:
Navigating Uncharted Waters: A roadmap to responsible innovation with AI in financial services
https://www.weforum.org/reports/navigating-uncharted-waters-a-roadmap-to-responsible-innovation-with-ai-in-financial-services
Navigating Uncharted Waters shows how financial services firms, policymakers and regulators and customers can overcome five risks and plot a course toward more rapid AI adoption.
ARTICLE #15:
Model Tuning and the Bias-Variance Tradeoff
http://www.r2d3.us/visual-intro-to-machine-learning-part-2/
Learn about bias and variance in our second animated data visualization.
ARTICLE #16:
Daniel Kahneman’s Strategy for How Your Firm Can Think Smarter
https://knowledge.wharton.upenn.edu/article/nobel-winner-daniel-kahnemans-strategy-firm-can-think-smarter/
The research is unequivocal, according to the father of behavioral economics: When it comes to decision-making, algorithms are superior to people.
ARTICLE #17:
Experience Doesn’t Predict a New Hire’s Success
https://hbr.org/2019/09/experience-doesnt-predict-a-new-hires-success
Is it time to rethink the way we assess job applicants?
ARTICLE #18:
So what is the difference between AI, ML and Deep Learning?
https://www.linkedin.com/pulse/so-what-difference-between-ai-ml-deep-learning-kanishka-mohaia/
The best ie simplest summation of this tech I have read (edited) linkedin.com. Once the domain of Sci-Fi geeks and film script writers, Artificial Intelligence or A.I.
ARTICLE #19:
Nudge management: applying behavioural science to increase knowledge worker productivity
https://jorgdesign.springeropen.com/articles/10.1186/s41469-017-0014-1
Knowledge worker productivity is essential for competitive strength in the digital century. Small interventions based on insights from behavioural science makes it possible for knowledge workers to be more productive. In this point of view article, we outline and discuss a new management style which we label nudge management. Nudge is a concept in behavioral science, political theory and behavioral economics which proposes positive reinforcement and indirect suggestions as ways to influence the behavior and decision making of groups or individuals. Nudging contrasts with other ways to achieve compliance, such as education, legislation or enforcement.
We liked reading this because it mirrored what we read from candidates every day after their receive ‘MyInsights, their personalised insights profile. We believe that every person regardless of their role craves personal growth. The feeling they have when they receive that report- priceless for our team. “Thank you for your email. I did find it useful as it has made me really think about my workplace and personal life by self-reflecting. I feel since reading this, I have stepped up in a few different situations including at work where I had stepped up in a temporary leadership role. Personally, I have been practising speaking my mind and let go of toxic friendships and make decisions more easily.”And … After getting the insight of what you see of me & your reasoning it made me think about work place moments & how well I’ve responded to situations as well as make me think about alternative ways I could have reacted & received differing outcomes.
ARTICLE #20:
Distilling BERT models with spaCy
https://towardsdatascience.com/distilling-bert-models-with-spacy-277c7edc426c
Transfer learning is one of the most impactful recent breakthroughs in Natural Language Processing. Less than a year after its release.
ARTICLE #21:
Building Trust in Machine Learning Models (using LIME in Python)
https://www.analyticsvidhya.com/blog/2017/06/building-trust-in-machine-learning-models/
This article helps us understand working of machine learning algorithms using LIME package. Using LIME, you can understand working of black box ML models.
ARTICLE #22:
Jordan Peterson on Workplace Performance, Politics & Faulty Myers-Briggs
Hilarious watching Jordan talking about selling personality assessments but mostly he is spot on in his observations.
Blog
Three diversity goals you can actually track and achieve in 2022
More money is flowing into Environmental, Social and Governance (ESG) than ever. In 2021, investors poured $649 billion into ESG-focused funds worldwide, up 90% from the $542 billion invested in 2020. In the UK, over 21% of investors plan to back funds and companies with comprehensive ESG strategies by 2025. And in Australia, more than 55% of super funds are using responsible investment approaches to inform strategic asset allocation.
All this investment has prompted a sharper focus on social issues across major companies – the S in Environmental, Social, and Governance. The great news is that investment in the big S, in turn, means more money and attention toward progress in Diversity, Equity and Inclusion (DEI).
Executives that care about diversity know that an effective strategy must start at the top – take Australian superannuation fund HESTA and its 40:40 vision as an example. But, to be truly successful, we need DEI goals at all levels, and we need to track, accurately, the degree to which we meet them.
Both boards and shareholders want measurable change in DEI, and fast. According to a Harvard Business Review study of S&P 500 earnings calls, the frequency with which CEOs talk about issues of equity, fairness and inclusion has increased by 658% since 2018. You can bet that this will only increase further in the coming years.
Diversity goals need to be measurable, today
According to another HBR article, 40% of US companies discussed DEI in their Q2 2020 earnings calls, which is a huge step up from the 4% of companies that did the year before. And with 1,600 CEOs pledging to take action on DEI, setting goals and tracking progress remain top priorities.
DEI and ESG are big challenges, and we might take myriad possible approaches in trying to solve them. Some companies may start at the executive level (HESTA, as an example), while others may invest in partnerships and outreach programs. The spectrum of options can easily become overwhelming.
| “Interestingly, I’m just looking at our workforce profile and have been discussing the changes in diversity since we updated our recruitment approach last March. Not only have we hired three times more ethnic minorities and 1.5 times more women, but we now have twice as many LGBTQI+ colleagues in our business than we did three years ago! Other initiatives have played a part, but I’d imagine the game changer has been Sapia as we’ve had some direct feedback from a transgender colleague that they felt more confident with our recruitment process than they did in other applications! David Nally, HR Manager, Woodie’s UK |
So why not start with the people you bring into your company, at all levels? Why not begin with the way you attract, assess, and select talent?
With a Smart conversational Ai, you can set realistic DEI targets and measure them, at scale, with little extra effort – ensuring you access the best talent from the widest possible pool. A Smart Interviewer is different to the simple chatbots used to automate routine tasks according to a fixed set of rules. For example, our conversational Ai is able to analyse interview responses to gain deeper insights about each candidate’s personality and competencies, in a fair and objective way.
Our Smart Interviewer helps you track and meet these three key diversity goals.
-
Gender bias
Our proprietary interview response database is made up of more than 500,000,000 words, enabling us to conduct the most sophisticated response analysis in the recruitment industry. We can do this on a macro scale (e.g. across countries, cultures, industries, and role types); or for individual companies.
Take these findings, combining data from a range of our customers, globally:
Figure 1: Gender stats across applicants, Ai recommendations and hired
Thanks to our machine-learning capabilities, and the size of our database, we can provide the hiring team with real-time analytics on the following parameters:
- Number of observed female applicants vs number of expected female applicants (and the same for male applicants)
- Overall hiring rate of females vs males
- Overall rate of female vs male recommendations made by our Ai
By employing a smart interviewing Ai at the first stage of recruitment, we can prove progress with regards to inclusivity and bias reduction. These aggregate company data show that while the expected number of female applicants exceeded the number of those that actually applied, the number of recommendations made by our Smart Interviewer also beat expectations (effectively compensating for the top-of-funnel bias). We can also see that the rate of observed female hires far exceeded the expected number.
What does this show? With just three metrics, you can see the progress being made in your recruitment process – and if performance is below expectation, you can see the stage at which targets are not being hit.
It is important to note that the recommendations of our Ai are based solely on its analysis of candidate responses in the chat-based interview. Its suitability criteria is based, among other factors, on HEXACO personality modeling and accurate assessments of various job related competencies such as team work, critical thinking and communication skills.
Our data also keep biases in check at each stage of the recruitment process, depending on the role type. As you can see, for all three roles, this company’s hiring outcomes were within regulatory limits (as stipulated by the US Equal Employment Opportunity Commission (EEOC)) across the three stages of their funnel: Applications received, recommendations made, and the hiring decisions ultimately made by the hiring team. The final step, it is important to note, happens independently of our Ai: It is a human decision. Despite this, the outcome data is recorded, so that the company can compare its outcomes against inputs and recommendations to see if late-funnel biases are occurring.
Figure 2: Role-type-based gender bias. Mid line indicates 0 bias. Shaded areas indicate the tolerance level. Right of line favours females and left favours males.
The feedback from candidates is extremely positive: Company A’s strivings for fairness and equality in its processes has resulted in a candidate satisfaction score of 98.7% for females, and 98.1% for males. Better still, the interview dropout rate across the board is less than 10%.
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Ethnicity bias
As with gender, our ethnicity analytics help hiring managers to easily set and accurately track goals for ethnicity representation in recruitment. Company A (whose data were shown in Figure 2) is, again, leading the way in this regard: Its BAME (Black, Asian and Ethnic Minorities) recommendation rate is at 46.5%, exceeding expectations – meanwhile, its non-BAME recommendation rate sits at 37.1%.
Our data has also helped Company A to increase its hiring commitments for First Nations people: The rate currently sits at 4.5%, from 4,000 candidates, above the national average of 1.8% (2018-19). This number is expected to increase over the coming year.
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Personality biases
The data we collect helps us, as well as our customers, understand the extent to which personality determines role suitability and general workplace success. It also helps us to eliminate long-standing biases that negatively impact certain candidates, despite the fact that said candidates may be highly suitable to the roles for which they are applying.
For example, people high in trait agreeableness (compassionate, polite, not likely to dissent or proffer controversial viewpoints) tend to underperform in the traditional face-to-face interviews. Hiring managers may assume, based on this, that they are unable to lead, or are not a ‘culture fit’. However, a face-value assessment of agreeableness is not a reliable predictor of candidate potential. Only scientific analysis of HEXACO traits can make this call with accuracy.
Take these two visualizations, showing how different personality traits affect the recommendations made by our Ai. Females (red dot) and males (blue dot) are slightly different in agreeableness, but there is virtually no difference in their conscientiousness, a strong predictor of job performance. As a result of being able to measure conscientiousness accurately, our system can effectively allow for higher levels of agreeableness – or cancel out the negative face-value judgements typically made in face-to-face interviews. Despite these personality differences, as shown in Figure 1, Sapia Ai recommendations for both male and female groups remain similar (~40%). This results in a fairer chance for all, and a wider pool of candidates. In this case, this is to the benefit of females.
Figure 3: Male (blue) and Female (red) personality trait differences
Bringing it all together
The world is changing, and we can no longer continue to take a “We’ll see what happens” approach to the ‘S’ in ESG. Many investors are pushing companies for better diversity and inclusion outcomes. At Sapia, our data show that fair, scientifically valid, and explainable Ai can produce better outcomes for peoples of all genders and ethnicities. The companies that have adopted our Ai approach are seeing strong improvement in their own DEI practices and results.
Over and above assisting our clients, our commitment to DEI is embodied in a guiding vision of our own: Our FAIR Framework. This embeds an approach that ensures our systems and processes are ethical and transparent. Many similar Ai systems operate in a ‘black box’, providing little knowledge about how their algorithms help make important decisions or create issues like amplifying biases. We are committed to a fairer world, free of bias – and, with every candidate interviewed, our data is bringing us closer.