Blog

Back

Written by Nathan Hewitt

Kallidus and Sapia = Faster, fairer hiring

An ‘unfair’ advantage is obtained for Recruiters by adding Sapia’s interview automation to Kallidus with faster, fairer and better hiring results.

Make a difference

As the first gate to employment, the hiring team has a huge influence on candidate experience, diversity and inclusion and overall business success. The way you hire can make someone’s day. It can set your business up to overtake the competition. It can be one step towards designing a fairer world for everyone.

Hiring is more complex than ever

There’s a lot expected of recruiters these days. Attracting candidates from diverse backgrounds and delivering exceptional candidate care whilst selecting from thousands of candidates isn’t easy.

Recruiters are expected to:

  • Find the right people, ensuring a diversity of candidates
  • Fill roles quickly and efficiently
  • Interrupt bias in hiring and promotion
  • Ensure every person hired amplifies the organisation’s values
  • Create a candidate experience that is engaging and rewarding

Technology is more powerful than ever

The good news is that technology has advanced to support recruiters. Integrating Sapia artificial intelligence technology with the powerful Kallidus ATS facilitates a fast, fair, efficient recruitment process that candidates love.

Now is the time to:

  • optimise recruiter time spent by 90%
  • increase candidate satisfaction to 99%
  • achieve interview completion (the equivalent of application fill) rates of 90%
  • reduce bias

Your advantage: Sapia + Kallidus

Gone are the days of screening CVs, followed by phone screens to find the best talent. The number of people applying for each job has grown 5-10 times in size recently. Reading each CV is simply no longer an option. In any case, the attributes that are markers of a high performer often aren’t in CVs and the risk of increasing bias is high.

We’ve created a quick, easy and fair hiring process that candidates love.

  1. Create a vacancy in Kallidus, and a Sapia interview link will be created.
  2. Include the link in your advertising. Every candidate will have an opportunity to complete a FirstInterview via chat.
  3. See results as soon as candidates complete their interview. Each candidate’s scores, rank, personality assessment, role-based traits and communication skills are available as soon as they complete the interview. Every candidate will receive automated, personalised feedback.

By sending out one simple interview link, you nail speed, quality and candidate experience in one hit.

Read: The Ultimate Guide to Interview Automation

To see it in action

Watch this 2 minute video to see how Sapia works inside Kallidus for Iceland Foods.

Start today

Get ahead with Sapia’s award-winning chat Ai available for all Kallidus users. Automate interview, screening, ranking and more, with a minimum of effort. Save time, reduce bias and deliver an outstanding candidate experience.

To keep up to date on all things “Hiring with Ai” subscribe to our blog!

You can try out Sapia’s Chat Interview right now – HERE. Else you can leave us your details to get a personalised demo


Blog

What is one big job HR is hired to do?  Manage Risk

Two recent events have brought HR back to their core role – managing risk.

In Australia, we have seen large numbers of staff getting underpaid in some sectors, such as retail and hospitality. For some of these businesses, it’s precipitated the collapse of the company. For others, it’s impacted their share price as these businesses make provisions for back pay in the hundreds of millions of dollars.

Globally – the coronavirus impacts on HR more than any other function. HR has to lead on managing health and safety for employees, guide organisations on remote working, support the CEO and leaders on their internal communications and public response to coronavirus, and a lot more.

Apart from those current risks garnering a lot of media attention, what business risks really ought to matter to HR? 

Ask a CEO of a sales/service business what they believe carries more risk to their bottom line – increasing turnover coupled with a high cost to hire, or a declining engagement score measured from a survey?

If you are hiring just 100 people a year, you can expect to LOSE 80 DAYS of work capacity to recruitment. Using automation tools like ours reduces that by 80% giving back 504 hours to the business. Click here to see case studies on time savings from using PredictiveHire.

HR managing business risk means tracking metrics like: 

1. Cost to hire – which should be the direct (the recruitment team for example) and indirect costs (the opportunity costs of all the people involved in recruitment)

2. Time to hire for any organisation that relies on frontline staff to deliver value- sales or customer service

3. Turnover especially early churn and non-regrettable churn, which is a good objective measure for quality of hire

4. The percentage of promotions within, only if you use hard objective data to make those decisions

5. Most of all, whether these metrics are going in the right direction quarter on quarter


Suggested Reading:

https://sapia.ai/blog/to-ai-or-not-to-ai/

Read Online
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 #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 productivit
y
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 sciencepolitical 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 educationlegislation 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.

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.

Read Online
Blog

Why you need to treat your candidates as you would your customers

If a new customer entered your store and was keen on buying something, you would never dream of ignoring them.

Even if they’re just browsing, you would not let them leave without trying to make a good impression on them. You’d try and win them over for next time they are looking to buy. You’d respect and thank them for thinking about you, and share knowledge with them about products you have, so that they leave better informed consumers. Maybe they’ll remember you the next time they have a purchase to make.

This same philosophy needs to apply to candidates who apply for jobs at your organisation. 

Yet, everyday we don’t … and it’s damaging. It’s damaging to both brands and to the people who apply to them. 

You need to treat your candidates as you do your customers. You need to treat them with respect, give them an interview experience that makes them feel comfortable, familiar and convenient, is fast, and dignifies the effort they have made in applying. Go further and give them feedback and insights about their strengths and weaknesses that they can use when looking at other jobs, it’s likely they will think of you in the future, and recommend you to their friends.   

What Qantas did right

As Michael Eizenberg, Head of Qantas Group Talent, Digital & Analytics told us: “We care deeply about two things when it comes to hiring. Firstly, diversity and inclusivity, and secondly the experience of everyone who comes into contact with the Qantas brand. Our goal is to treat every candidate like we would a customer.” 

Qantas metrics prove the value of treating candidates as customers.

  • Application completion is at 93%, a phenomenal outcome. (Completion rate of the video interview previously used was 50%.)
  • 98% of candidates report the feedback they are provided is helpful and accurate.

What to look out for

The idea of creating positive candidate experiences is not new, but the global talent shortage has empowered candidates in a way that companies are no longer the ones wielding the power. 

You’re not doing the choosing. Candidates are. They are assessing you at every step of the way in a recruitment process. 

We need to treat candidates not just as ‘prospective employees’, but put on the best show as “prospective employers”. We need to roll out the red carpet and listen to their needs – from the first  moment they interact with us.

We’ve heard about the great resignation across the globe as people have reassessed their lives and decided they want more from their job than just a steady paycheck.

People looking for jobs not only have more choices, but they also possess more information about companies thanks to technology like Glassdoor. They will likely do research on your company before they apply.

Much like shopping has changed the way people buy things, making online comparisons and reading reviews, the internet has created a similar opportunity for job seekers who are looking for the best place to work. 

Organisations need to not only consciously articulate and promote the value they offer and why people should consider working for them – they actually have to prove it through their recruitment process. 

The candidate is a consumer of your “product’ (your workplace and everything you stand for), or at least you need to think of them as one.

This means making people feel valued by your company even before they work there. 

 You can read how Qantas’ approach to treat candidates as customers has improved the quality and retention on their candidates here.

Read Online