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An HR Algorithm Can Tell How Often You Will Change Jobs

Job-hopping algorithm: Assessing Job-Hopping Attitudes From Chat Interview

We have no survivable and sustainable future without science, just as we do not have you without it. 

Since the start of the coronavirus epidemic, many companies have turned to smart algorithms to find out who is the best candidate for open positions. Most often, face-finding programs, games, quizzes, and software that examines other visual or linguistic patterns are used to decide who is included in the interview circle.

An Australian company called Sapia (Formerly PredictiveHire), founded in October 2013, appears to have gone much further. It has developed a machine-learning algorithm to assess the likelihood of frequent job changes for a given candidate. –  MIT Technology Review. 

According to Barbara Hyman, CEO of HR, their clients are employers who have to process a lot of application. Also they are active in the areas of customer service, retail, sales or healthcare, among others.

In the first round, a chatbot decides on the applicants

When someone applies for a job through an HR company, they must first “convince” a chatbot of their values. The algorithm asks a series of open-ended questions and analyses personality traits such as initiative, intrinsic motivation, or resilience.

Moreover, the algorithm may examine the likelihood of frequent job changes in the future – or, as advertised on the Sapia website, the “ risk of escape ” – even for fully career candidates. The focus of the HR company’s latest study is to develop a machine learning algorithm that specifically seeks to predict this. The research examined 45,899 candidates. They had previously answered 5-7 open-ended questions about their experiences and situational awareness through the Sapia chatbot.

The chatbot asked for personality traits that, based on Sapia’s own research, may be closely related to frequent job changes. For example the traits could be -greater openness to new experiences or lack of practicality.

Algorithms against wage increases

Nathan Newman, an associate professor at John Jay College of Criminal Justice in New York who wrote a study in 2017 on how large-sample data analysis can be used to break wages in addition to discriminating against employees, told MIT Technology Review Recent work by Sapia.

This includes the increasingly popular personality tests based on machine learning, which seek to screen out potential workers who are more likely to support unionisation or are more likely to ask for wage increases. According to MIT Technology Review, employers are increasingly keeping an eye on their employees ’emails, online chats, and data they can use to filter out whether a particular colleague is about to leave. All this so they can calculate the minimum wage increase is and where appropriate, they may be allowed to remain.

Uber’s algorithm-based management systems are said to seek to keep employees away from offices and digital locations in a way that they can’t even accidentally organize and collectively demand better pay or treatment.


Sapia has found a relationship between the language people use and their attitudes towards job-hopping.

If a simple automated chat interview can infer a candidate’s likelihood of job-hopping, it presents significant opportunities, especially when assessing candidates with no prior work history.

This work shows that the language one uses when responding to interview questions related to situational judgment and past behaviour is predictive of their likelihood to job hop. This paper explores:

  • Research around self-initiated job hopping
  • Correlation between language and job-hopping likelihood
  • NLP methods that can be used to represent language

Find out how you can identify job-hopping attitudes before you hire.  To get your copy of the Research Paper click here.


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Finally, you can try out Sapia’s Chat Interview right now, or leave us your details to get a personalised demo

Also, have you seen the 2020 Candidate Experience Playbook? Download it here.


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Thank you, Michelle Obama 

Becoming, by Netflix tells the Michelle Obama story, and throughout the documentary, it is clear that other people’s stories resonate with her just as much as her story resonates with them. As inspiring as you would expect her to be, she spends much time mentoring and coaching young women, just by listening to them and sharing her story. Midway through the doco, as another young African American woman shares her self-doubt because she doesn’t have all the reference-able facts to open up the right doors, (the right college on her CV, the right GPA, etc. ). Michelle Obama says this: 

  ‘We focus too much on stats and not enough on stories.’

Wow! That line just nailed it for us because your story of what makes you you. What shapes and motivates you is what matters not how you turn up to your education, to an interview, to your job.

It’s why CVs need to die.

It’s why so many organisations are investing in testing your softs skills, the real skills because hard skills can be learnt. Your openness to new ideas, ability to cope with change, humility to ask for help, are way more relevant than ‘your stats’ at any point in time. That means two things for HR: Finding technology that will help you understand the story and removing bias that gets in the way of being able to hear the story.

Finding your story 

COVID-19 enforced WFH restrictions have created zoom fatigue. It’s a real thing. 

Eight weeks and already we are so over video.

Text has been around for a decade. Ever heard of text fatigue? No, that’s because text is easy, it is fast (especially if you are a 16-year-old who texts in acronyms (our latest fav ‘POS’ (not point of sale but parent over shoulder)). It’s also safe. Safe for introverts, safe for people who might not feel comfortable on a video call or even worse a video interview. 

Forcing your applicants to invest in impression management is not a good start to building a relationship of trust and authenticity with your newest employee. How many great introverts, deep-thinkers and high-integrity individuals are you at risk of losing when you force people to perform on a video interview? 

And why would you make people play a game, answer 150 +multi choice questions, (many repetitive that gives your experience no platform at all), when you can make it easy and comfortable with a chat or text interview?

Do a text interview

Doing it by text gives everyone a chance to shine, without performance anxiety, without having to prepare or risk someone gaming it by googling the right answer. When you connect with people about them, using technology they trust, that lets them be themselves (without bias getting in the way). That is what a candidate first experience looks like. It’s why we get 99% + candidate satisfaction from 10,000 applicants a month. 


Fall in love with a chat-based interview here 


  

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Algorithmic Hiring to Improve Social Mobility

It is a widely held belief that diversity brings strength to the workplace through different perspectives and talents.

The value is greatest when companies harness the differences between employees from multiple demographic backgrounds to understand and appeal to a broad customer base. But true diversity relies on social mobility and therein lies the problem: the rate of social mobility in the UK is the worst in the developed world.

The root cause of the UK’s lack of social mobility can be found in the very place that it can bring the most value – the workplace. Employers’ recruiting processes often suffer from unconscious human bias that results in involuntary discrimination. As a result, the correlation between what an employee in the UK earns today and what his or her father earned is more apparent than in any other major economy.

This article explores the barriers to occupational mobility in the UK and the growing use of predictive analytics or algorithmic hiring to neutralise unintentional prejudice against age, academic background, class, ethnicity, colour, gender, disability, sexual orientation and religion.

The government wants to promote equal opportunity

The UK government has highlighted the fact that ‘patterns of inequality are imprinted from one generation to the next’ and has pledged to make their vision of a socially mobile country a reality. At the recent Conservative party conference in Manchester, David Cameron condemned the country’s lack of social mobility as unacceptable for ‘the party of aspiration’. Some of the eye-opening statistics quoted by Cameron include:

  • 7% of the UK population has been privately educated.
  • 22% of FTSE 350 chief executives have been privately educated.
  • 44% within the creative industries have been privately educated.
  • By the age of three, children from disadvantaged families are already nine months behind their upper middle class peers.
  • At sixteen, children receiving school meals will on average achieve 1.7 grades lower in their GCSEs.
  • For A levels, the school one attends has a disproportionate effect on A* level achievement; 30% of A* achievers attend an independent school, while children attending such schools make up merely 7% of the general population.
  • Independent school graduates make up 32% of MPs, 51% of medics, 54% of FTSE 100 chief executives, 54% of top journalists and 70% of High Court judges.
  • By the age of 42, those educated privately will earn on average £200,000 more than those educated at state school.

Social immobility is an economic problem as well as a social problem

The OECD claims that income inequality cost the UK 9% in GDP growth between 1990 and 2010. Fewer educational opportunities for disadvantaged individuals had the effect of lowering social mobility and hampering skills development. Those from poor socio economic backgrounds may be just as talented as their privately educated contemporaries and perhaps the missing link in bridging the skills gap in the UK. Various industry sectors have hit out at the government’s immigration policy, claiming this widens the country’s skills gap still further.

Besides immigration, there are other barriers to social mobility within the UK that need to be lifted. Research by Deloitte has shown that 35% of jobs over the next 20 years will be automated. These are mainly unskilled roles that will impact people from low incomes. Rather than relying too heavily on skilled immigrants, the country needs to invest in training and development to upskill young people and provide home-grown talent to meet the future needs of the UK economy. Countries that promote equal opportunity for everyone from an early age are those that will grow and prosper.

How are employers supporting the government’s social mobility policy?

The UK government’s proposal to tackle the issue of social mobility, both in education and in the workplace, has to be greatly welcomed. Cameron cited evidence that people with white-sounding names are more likely to get job interviews than equally qualified people with ethnic names, a trend that he described as ‘disgraceful’. He also referred to employers discriminating against gay people and the need to close the pay gap between men and women. Some major employers – including Deloitte, HSBC, the BBC and the NHS – are combatting this issue by introducing blind-name CVs, where the candidate’s name is blocked out on the CV and the initial screening process. UCAS has also adopted this approach in light of the fact that 36% of ethnic minority applicants from 2010-2012 received places at Russell Group universities, compared with 55% of white applicants.

Although blind-name CVs avoid initial discriminatory biases in an attempt to improve diversity in the workforce, recruiters may still be subject to similar or other biases later in the hiring process. Some law firms, for example, still insist on recruiting Oxbridge graduates, when in fact their skillset may not correlate positively with the job or company culture. While conscious human bias can only be changed through education, lobbying and a shift in attitude, a great deal can be done to eliminate unconscious human bias through predictive analytics or algorithmic hiring.

How can algorithmic hiring help?

Bias in the hiring process not only thwarts social mobility but is detrimental to productivity, profitability and brand value. The best way to remove such bias is to shift reliance from humans to data science and algorithms. Human subjectivity relies on gut feel and is liable to passive bias or, at worst, active discrimination. If an employer genuinely wants to ignore a candidate’s schooling, racial background or social class, these variables can be hidden. Algorithms can have a non-discriminatory output as long as the data used to build them is also of a non-discriminatory nature.

Predictive analytics is an objective way of analysing relevant variables – such as biodata, pre-hire attitudes and personality traits – to determine which candidates are likely to perform best in their roles. By blocking out social background data, informed hiring decisions can be made that have a positive impact on company performance. The primary aim of predictive analytics is to improve organisational profitability, while a positive impact on social mobility is a healthy by-product.

An example of predictive analytics at work

A recent study in the USA revealed that the dropout rate at university will lead to a shortage of qualified graduates in the market (3 million deficit in the short term, rising to 16 million by 2025). Predictive analytics was trialled to anticipate early signs of struggle among students and to reach out with additional coaching and support. As a result, within the state of Georgia student retention rates increased by 5% and the time needed to earn a degree decreased by almost half a semester. The programme ascertained that students from high-income families were ten times more likely to complete their course than those from low-income households, enabling preventative measures to be put in place to help students from socially deprived backgrounds to succeed.

What can be done to combat the biases that affect recruitment?

Bias and stereotyping are in-built physiological behaviours that help humans identify kinship and avoid dangerous circumstances. Such behaviours, however, cloud our judgement when it comes to recruitment decisions. More companies are shifting from a subjective recruitment process to a more objective process, which leads to decision making based on factual evidence. According to the CIPD, on average one-third of companies use assessment centres as a method to select an employee from their candidate pool. This no doubt helps to reduce subjectivity but does not eradicate it completely, as peer group bias can still be brought to bear on the outcome.

Two of the main biases which may be detrimental to hiring decisions are ‘Affinity bias’ and ‘Status Quo bias’. ‘Affinity bias’ leads to people recruiting those who are similar to themselves, while ‘Status Quo bias’ leads to recruitment decisions based on the likeness candidates have with previous hires. Recruiting on this basis may fail to match the selected person’s attributes with the requirements of the job.

Undoubtedly it is important to get along with those who will be joining the company. The key is to use data-driven modelling to narrow down the search in an objective manner before selecting based on compatibility. Predictive analytics can project how a person will fare by comparing candidate data with that of existing employees deemed to be h3 performers and relying on metrics that are devoid of the type of questioning that could lead to the discriminatory biases that inhibit social mobility.

“When it comes to making final decisions, the more data-driven recruiting managers can be, the better.”

Bias works on many levels of consciousness

‘Heuristic bias’ is another example of normal human behaviour that influences hiring decisions. Also known as ‘Confirmation bias’, it allows us to quickly make sense of a complex environment by drawing upon relevant known information to substantiate our reasoning. Since it is anchored on personal experience, it is by default arbitrary and can give rise to an incorrect assessment.

Other forms of bias include ‘Contrast bias’, when a candidate is compared with the previous one instead of comparing his or her individual skills and attributes to those required for the job. ‘Halo bias’ is when a recruiter sees one great thing about a candidate and allows that to sway opinion on everything else about that candidate. The opposite is ‘Horns bias’, where the recruiter sees one bad thing about a candidate and lets it cloud opinion on all their other attributes. Again, predictive analytics precludes all these forms of bias by sticking to the facts.

https://sapia.ai/blog/workplace-unconscious-bias/

Age is firmly on the agenda in the world of recruitment, yet it has been reported that over 50% of recruiters who record age in the hiring process do not employ people older than themselves. Disabled candidates are often discriminated against because recruiters cannot see past the disability. Even these fundamental stereotypes and biases can be avoided through data-driven analytics that cut to the core in matching attitudes, skills and personality to job requirements.

Once objective decisions have been made, companies need to have the confidence not to overturn them and revert to reliance on one-to-one interviews, which have low predictive power. The CIPD cautions against this and advocates a pure, data-driven approach: ‘When it comes to making final decisions, the more data-driven recruiting managers can be, the better’.

The government’s strategy for social mobility states that ‘tackling the opportunity deficit – creating an open, socially mobile society – is our guiding purpose’ but that ‘by definition, this is a long-term undertaking. There is no magic wand we can wave to see immediate effects.’ Being aware of bias is just the first step in minimising its negative effect in the hiring process. Algorithmic hiring is not the only solution but, if supported by the government and key trade bodies, it can go a long way towards remedying the inherent weakness in current recruitment practice. Once the UK’s leading businesses begin to witness the benefits of a genuinely diverse workforce in terms of increased productivity and profitability, predictive hiring will become a self-fulfilling prophecy.

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These 6 start-ups will be in the ‘spotlight’ at Spring HR Tech

The pandemic hasn’t slowed down innovation in HR tech

By:  | March 1, 2021 • 2 min read

While the past year has brought considerable challenges to the HR function, there is one silver lining: Innovation in HR tech is abounding. Despite the disruptions of the pandemic, the HR tech market has continued to thrive—with many new entrants tailoring solutions to the unique HR needs that have arisen in recent months, says Steve Boese, chair of the HR Technology Conference, which will be held in Las Vegas in the fall.

Steve Boese

“The HR technology start-up space has been extremely vibrant for years, and the pandemic, it seems to me, has not really slowed the pace of innovation very much if at all,” Boese says. “Newer, more agile tech companies can often provide important and immediate benefits to help organizations react quickly to a changing environment.”

Boese will share several of the most innovative solutions during a Spotlight Session at this month’s Spring HR Tech, a free and virtual event. Boese and conference organizers reviewed about 75 start-ups, conducting demos and meetings with about 30 of them, to ultimately select six standout start-ups that will demo during the conference session. The session, Six Emerging HR Tech Startups to Put on Your Radar Now, will begin at 2 p.m. Friday, March 19.

“These six showcased innovation, relevancy, impact and leading-edge technology for HR organizations that we felt represented a great selection of the best in new thinking in HR tech,” Boese says.

Although the start-ups address a range of issues facing HR, their work is being uniquely driven by recent events.

“As you would expect, the impact of the events of 2020—the pandemic and the social justice movement in particular—are definitely influencing the technology developments we are seeing,” he says. “So, areas like mental health and wellbeing, diversity and inclusion and even support for offboarding employees are three specific areas that will be showcased in the session.”

The participating companies are:

Unmind: a technology solution employers can use to support their overall mental health programs and strategies

FutureFit AI: a new approach to separations, offering people a more supportive and personalized experience as they transition to their next role

Hourly by AMS: a set of tools to help both organizations and candidates navigate the hiring process for hourly roles

Sapia (Formerly PredictiveHire) : a fully digital software solution for volume recruitment

Eskalera: a platform that drives employee inclusion through training, reflection and connection

Work Shield: a tool that manages employers’ reporting, investigation and resolution of workplace harassment and discrimination issues in their entirety

Click HERE to register for Spring HR Tech


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, or leave us your details to get a personalised demo

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