Written by Nathan Hewitt

When unconscious bias becomes a conscious bias

unconscious bias

A perfect example of unconscious bias manifesting in a conscious and damaging way.

At Sapia we are attuned to research and stories around bias – for most of us, it’s the reason we work here.

Our team has observed the speed with which the blame for Coronavirus has targeted an entire ethnicity.

In this case, I’ve heard some say, “it’s not racism, people are genuinely scared of the spread of the virus. It’s a deadly virus. As it originated in China people naturally worry about anyone from China”.

Unfortunately, this is the very definition of bias.

A flawed logic that seems sensible on the surface, nothing but pure stereotyping underneath. Simply, everyone who looks Chinese are not recent travels from China.

Australia is home to 1.2mil Chinese origin Australians according to the 2016 Census. Should we worry about all of them? Bias has no place in fighting any problem, even when it is a deadly virus. It only creates stress and disharmony.

The irony is this: the virus is a true fair operator. It has no racial preferences.  

At the beginning of this week, one of our team who had come down with a cold shared he would work from home, to keep the team safe from his contagion.

We laughed at the time about him being a carrier of Coronavirus.  By the end of the week, members of our team with holidays booked to visit family and travel in China during the Easter break had cancelled their trip.

They did this before Qantas stopped their direct flights and before the Australian government announced that Chinese people won’t be allowed back into Australia.

The team member who had a cold this week is Sri Lankan by birth. I guess that means we would have all been safe if he turned up to work as he is the ‘right’ ethnicity.

We are on a mission: To solve issues of bias in hiring

As a white immigrant myself, I don’t experience those prejudices. I have had career and life opportunities beyond my dreams, unfettered by racial bias.

Building a technology that gives equivalence to such career opportunities is why we work for our company. Some of our team have been screened out of job openings. Maybe they had the wrong name, went to the wrong school or just didn’t look like a cultural fit?

Unfortunately, AI hiring tools can be biased 

Not all AI is equal. HireVue, an AI-driven recruitment company, has recently been taken to the US Federal Trade Commission with a prominent rights group claiming unfair and deceptive trade practices in HireVue’s use of face-scanning technology to assess job candidates’ “employability.”

Using video is an obvious problem as a data source for reasons around race and gender and their associated biases, but you might be surprised to know that CV’s can be just as flawed and are in much broader use as a first parse for algorithms.

How does AI solve the issues of discrimination and bias in recruitment?

At Sapia, we rely on a simple open, transparent interview via a text conversation to evaluate someone for a role. No visuals, no CV data. No voice data as that too carries the risk of bias. Neither do we take data from Facebook. Using nothing that the candidate does not know about.

Bottom line, testing for bias and removing it from algorithms is possible. Whereas for humans, it’s not.

If you want to learn more about how we test for bias and why bias testing is critical to an AI screening tool get in touch here.

No amount of bias training will make you less biased.  Maybe that’s one reason why using machines to augment and challenge decisions is fast becoming mainstream.

It certainly helps to reduce the impact of unconscious bias in hiring decisions.




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.

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

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5 terrible job interview questions, and 5 science-backed alternatives

5 top job interview questions | Sapia Ai interview software

Not all interview questions are created equal. If you’re a veteran recruiter or talent acquisition manager, this may sound obvious, but the reality is that many companies (and their hiring managers) run interviews like an informal conversation, in which so-so questions are selected at random, and candidate responses are recorded haphazardly. 

This has led to a general lack of confidence in interviewing as a practice: An Aptitude Research and report from 2022 found that 33% of companies aren’t confident in the way they interview, and 50% believe they’ve lost talent due to their processes. Statistically speaking, it’s likely that your company or clients fit into these cohorts.

There are two proven fixes to shoddy interviewing: The right structure, and the right questions. Before we share our list of interview questions as recommended by our personality scientists, let’s quickly cover the structured interview and its benefits.

Why you should only be using structured interviews

Many (if not most) of us believe that resumes and past experience are the best indicators of future employee performance. Surprisingly, that’s not true: The best way to find top candidates (with 26% of predictive success) is the structured interview; conversely, past job experience accurately predicts just 3% of an employee’s on-the-job performance.

When you run structured interviews, you devise a consistent set of questions that are asked to all candidates without any variation. Responses to questions are generally entered into a rubric, and are then scored against a consistent criteria. The benefits of structured interviews are numerous, but the crucial reason for using them is that you’re minimising variables; all candidates are compared fairly to one another.

Check out this quick video to learn more about the benefits of structured interviewing.

Our steadfast belief that past experience dictates the future, while understandable, has led to the proliferation of academic questions that trap and stymie candidates, mainly because they’re based on difficult concepts that are near-on impossible to articulate without preparation. 

For example, it’s not uncommon for marketing candidates to be asked something like, “Tell me how you’d adjust ROAS (Return on Ad Spend) numbers to compensate for unexpected reduction in impression share”. Should the candidate know what ROAS and impression share are? Sure. Can you reasonably expect them to solve an unexpected puzzle while you stare them down? No.

Instead, you want to ask questions that speak to behaviours. And, ideally, you want a reliable framework for assessing responses so you can adequately analyse behaviours (more on this later).

  1. Don’t ask: Tell me how you’d respond if [specific situation] occurred.
    Instead, ask: Tell me about when something went wrong at work, and you had to fix it. How did you go about it?

    This is similar to the marketing example above. Although it’s tempting, you want to avoid questions that attempt to gauge technical proficiency. Such questions are best saved for pre- or post-interview assessments.

  2. Don’t ask: Why do you want this job?
    Instead, ask: If you could do anything in the world, what might it be?

    No one has ever had an original response to the question of ‘why’. Responses are laden with bias, and most hiring managers expect some grandiose (and frankly, sycophantic) response. Conversely, the question of ‘if’ is far more telling – it speaks to a candidate’s openness, which is a valuable (and measurable) trait for problem-solving.

  3. Don’t ask: What would your former manager say about you?
    Instead, ask: Describe a time when you struggled to build a relationship with someone important. How did you eventually overcome that?

    Former managers are not reliable character witnesses when their feedback is relaid second-hand by the candidate. You’ll never get a true response here. Instead, try to understand how a candidate overcomes difficult relationships. Answers will clue you in to a candidate’s levels of emotionality, extraversion, and empathy.

  4. Don’t ask: What is your biggest weakness?
    Instead, ask: Tell me about a situation in which you have had to adjust to changes over which you had no control. How did you handle it?

    Candidates will not accurately describe their weaknesses to you. As with the questions above, you’re asking a question that will result in a biased sample. Instead, ask about a common point of pressure that we all experience in working life. If someone is adaptable and flexible to change, their responses will show you – and you’ll have learned something much more valuable.

  5. Don’t ask: What is your biggest strength?
    Instead, ask: How would you change an institutional “this is always how we do it” attitude, if you felt there was a better approach?

    These questions don’t seem interchangeable on the face of it, but it’s far more valuable to know how a candidate can use their skills and behaviours to propel your company forward, than to know how they’d big-up themselves. Legacy thinking is a company-killer, and you want people who can think laterally to solve problems with new solutions. So find out if they can do that.

Go beyond great questions

These questions will serve you well in your interviews, but at a certain point, they’re only as good as your interview structure and analysis capabilities. Sapia’s Ai Smart Interviewer is designed to ask these questions – plus hundreds of others – and assess candidate responses to build accurate behavioural profiles. This means you get high quality candidates without having to bother with a long and inconsistent interview process.

Want even more questions designed by our people scientists and proven in the field? Download our HEXACO job interview rubric for free, here.

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Egy HR-algoritmus Képes Megmondani, Milyen Gyakran Fogsz Munkát Váltani

A koronavírus-járvány kezdete óta számos vállalat fordult okos algoritmusokhoz, hogy kiderítse, ki a legjobb jelölt a nyitott pozíciókra. Leggyakrabban arckereső programokat, játékokat, kvízeket és más vizuális vagy nyelvi mintázatokat vizsgáló szoftvereket vetnek be, hogy eldöntsék, ki kerül be az interjúkörbe.

A jelek szerint a 2013 októberében alapított, ausztrál PredictiveHire nevű cég ennél is sokkal tovább ment: olyan gépi tanuláson alapuló algoritmust fejlesztett, amellyel felmérhető, hogy egy adott jelölt esetén mekkora a gyakori munkahelyváltás valószínűsége – írta a héten az MIT Technology Review.

Barbara Hyman, a HR-cég ügyvezető igazgatója szerint ügyfeleik olyan munkáltatók, akiknek rengeteg jelentkezést kell feldolgozniuk, és egyebek mellett az ügyfélkiszolgálás, a kiskereskedelem, az értékesítés vagy az egészségügy területén aktívak.

Első körben chatbot dönt a jelentkezőkről

Amikor valaki a HR-cégen keresztül jelentkezik állásra, először egy chatbotot kell „meggyőznie” értékeiről. Az algoritmus nyitott kérdések sorát teszi fel, és olyan személyiségjegyeket elemez, mint a kezdeményezőkészség, a belső motiváció vagy az ellenálló képesség.

Sőt, az algoritmus a jövőben a gyakori munkahelyváltás valószínűségét – vagy ahogy a PredictiveHire honlapján reklámozza, a „menekülés kockázatát” – is vizsgálhatja, még teljesen pályakezdő jelöltek esetén is. A HR-cég legújabb tanulmányának fókuszában ugyanis egy olyan gépi tanuló algoritmus fejlesztése áll, amely kifejezetten ezt igyekszik előre megmondani. A kutatás keretében 45899 jelöltet vizsgáltak meg, akik korábban a PredictiveHire chatbotján keresztül válaszoltak a tapasztalataikról és helyzetmegítélő képességeikről szóló 5-7 nyitott kérdésre.

Ezek olyan személyiségjegyekre kérdeztek rá, amelyek korábbi kutatások – például a PredictiveHire saját kutatása – alapján szoros összefüggésben lehetnek a gyakori munkahelyváltásokkal, például az új élmények iránti nagyobb nyitottság vagy a gyakorlatiasság hiánya.

Algoritmusok a béremelés ellen

Nathan Newman, a New York-i John Jay College of Criminal Justice egyik egyetemi docense, aki 2017-ben arról írt tanulmányt, hogy a nagymintás adatelemzés a munkavállalók diszkriminációján felül hogyan használható a bérek letörésére, az MIT Technology Review-nak azt mondta, a PredictiveHire legutóbbi munkája

az egyik legkártékonyabb módja a big data munkaügyi alkalmazásának.

Ide tartoznak a gépi tanuláson alapuló, egyre népszerűbb személyiségtesztek is, amelyek azokat a potenciális munkavállalókat igyekeznek kiszűrni, akik nagyobb valószínűséggel támogatnák a szakszervezetekbe tömörülést, vagy hajlamosabbak béremelést kérni. Mindezt úgy, hogy az MIT Technology Review szerint a munkáltatók egyre jobban szemmel tartják dolgozóik e-mailjeit, online beszélgetéseit és minden olyan adatot, amelyből leszűrhetik, hogy az adott kolléga távozni készül-e, és kiszámolhatják, mi az a minimális béremelés, amellyel még adott esetben maradásra bírhatják.

Az Uber algoritmus alapú menedzsment rendszerei állítólag úgy igyekeznek távol tartani a munkatársakat az irodáktól és a digitális helyszínektől, hogy még véletlenül se tudjanak szervezkedni és kollektíven jobb fizetést vagy bánásmódot követelni.

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