Written by Nathan Hewitt
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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Reducing disability bias with chat-based interviews
This research paper is part of our accepted submission to SIOP, and will be presented at the 2023 SIOP Conference in Boston.
Bias and discrimination against candidates and employees with disabilities continues to be an increasingly important topic 30 years after the Americans with Disabilities Act of 1990 (ADA) was passed. The unemployment rate for those with a disability (10.1%) in 2021 was about twice as high as the rate for those without a disability (5.1%) (U.S. Bureau of Labor Statistics, 2022).
So what are the barriers for individuals with disabilities trying to gain employment and how can they be reduced or eliminated?
Human bias
Traditional face-to-face or video interviews in particular create potential barriers for individuals with disabilities due to the well-documented stigma and prejudice against those with disabilities (Scior, 2011; Thompson et al., 2011). An experimental study found less interest for job applicants that disclosed a disability, despite being equally qualified (Ameri et al., 2015).
Poor candidate experience
Another concern is that certain selection methods may cause candidates with disabilities stress or anxiety, therefore not allowing them to put their best foot forward. For example, one study found less than 10% of those with Autism Spectrum Disorder believe they’re able to demonstrate their skills and abilities with in-person or video interviews (Cooper & Kennady, 2021).
Accessibility concerns
Candidates with disabilities may also struggle with timed online assessments (Hyland & Rutigliano, 2013). For example, candidates with dyslexia or other learning and language disabilities may struggle with reading or spelling and may need extra time.
Sapia’s approach to removing these barriers is our blind, online, untimed, chat-based interview that can not only help reduce discrimination against those with disabilities but also create a more positive candidate experience for them. This format is particularly helpful for individuals with disabilities where traditional in-person interviews, video interviews, or timed assessments may cause stress or discomfort, therefore not allowing them to adequately demonstrate their skills.
We examined the adverse impact statistics (effect size, 4/5ths ratio, and Z-test) for over 15,000 candidates applying to a retail store associate role who self-reported having a disability, compared to those who reported no disability. We found no major or consistent adverse impact flags for the full sample of candidates with a disability or the majority of individual disability groups.
Additionally, candidates with disabilities had positive reactions to the chat-interview, with a candidate happiness score of 8.9/10 and 95.8% leaving either a positive or neutral comment (For example, “Being dyslexic, this interview gives me a fantastic opportunity to think and re-read my responses before delivery.” and “I really enjoyed this unique interview experience. I am autistic so voice and face-to-face interviews have always been a bit daunting, but this felt natural and enjoyable.”)
This research demonstrates that using online, untimed, chat-based interviews could help reduce bias and discrimination against candidates with disabilities. Additionally, examining score differences and candidate reactions by type of disability can help guide product enhancements to make the experience even more enjoyable, accessible, and fair.
References:
Ameri, M., Schur, L., Adya, M., Bentley, S., McKay, P., & Kruse, D. (2015). The disability employment puzzle: A field experiment on employer hiring behavior. National Bureau of Economic Research (NBER) Working Paper Series, Working Paper 21560.
Cooper, R., & Kennady, C. (2021). Autistic voices from the workplace. Advances in Autism, 7(1), 73–85.
Hyland, P., & Rutigliano, P. (2013). Eradicating Discrimination: Identifying and Removing Workplace Barriers for Employees With Disabilities. Industrial and Organizational Psychology, 6(4), 471-475.
Scior, K. (2011). Public awareness, attitudes and beliefs regarding intellectual disability: A systematic review. Research in Developmental Disabilities, 32(6), 2164-2182.
Thompson, D., Fisher, K., Purcal, C., Deeming, C., & Sawrikar, P. (2011). Community attitudes to people with disability: Scoping project No. 39). Australia: Disability Studies and Research Centre, University of New South Wales.
U.S. Bureau of Labor Statistics (2022). Persons with a Disability: Labor Force Characteristics— 2021. News Release USDL-22-0317, U.S. Bureau of Labor Statistics, Feb 24.
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An AI hiring firm says it can predict job hopping based on your interviews
An AI hiring firm says it can predict job-hopping based on your interviews. The idea of “bias-free” hiring, already highly misleading, is being used by companies to shirk greater scrutiny for their tools’ labor issues beyond discrimination.
Since the onset of the pandemic, a growing number of companies have turned to AI to assist with their hiring.
The most common systems involve using face-scanning algorithms, games or other evaluations to help determine which candidates to interview.
Activists and scholars warn that these screening tools can perpetuate discrimination. However, the makers themselves argue that algorithmic hiring helps correct for human biases.
Algorithms can be tested and tweaked, whereas human biases are much harder to correct—or so the thinking goes.
In a December 2019 paper, researchers at Cornell reviewed the landscape of algorithmic screening companies to analyze their claims and practices. Of the 18 they identified with English-language websites, the majority marketed as a fairer alternative to human-based hiring. Thus suggesting that they were latching onto the heightened concern around these issues to tout their tools’ benefits and get more customers.
But discrimination isn’t the only concern with algorithmic hiring. Some scholars worry that marketing language that focuses on bias lets companies off the hook on other issues, such as workers’ rights. A new preprint from one of these firms serves as an important reminder. “We should not let the attention that people have begun to pay to bias/discrimination crowd other issues,” says Solon Barocas, an assistant professor at Cornell University and principal researcher at Microsoft Research, who studies algorithmic fairness and accountability.
The firm in question is Australia-based Sapia (Formerly PredictiveHire), founded in October 2013.
It offers a chatbot that asks candidates a series of open-ended questions. It then analyses their responses to assess job-related personality traits like “drive,” “initiative,” and “resilience.”
According to the firm’s CEO, Barbara Hyman, its clients are employers that must manage large numbers of applications, such as those in retail, sales, call centers, and health care.
As the Cornell study found, it also actively uses promises of fairer hiring in its marketing language. On its home page, it boldly advertises: “Meet Smart Interviewer – Your co-pilot in hiring. Making interviews super fast, inclusive and bias free.
As we’ve written before, the idea of “bias-free” algorithms is highly misleading. But Sapia’s latest research is troubling for a different reason. It is focused on building a new machine-learning model that seeks to predict a candidate’s likelihood of job-hopping. That is the practice of changing jobs more frequently than an employer desires. The work follows the company’s recent peer-reviewed research that looked at how open-ended interview questions correlate with personality.
The study used the free-text responses from 45,899 candidates who had used Sapia’s chatbot.
Applicants had originally been asked five to seven open-ended questions and self-rating questions about their past experience and situational judgment.
These included questions meant to tease out traits that studies have previously shown to correlate strongly with job-hopping tendencies, such as being more open to experience, less practical, and less down to earth. The company researchers claim the model was able to predict job hopping with statistical significance. Sapia’s website is already advertising this work as a “flight risk” assessment that is “coming soon.” Sapia’s new work is a prime example of what Nathan Newman argues is one of the biggest adverse impacts of big data on labor.
Machine learning for the win!
Machine-learning-based personality tests, for example, are increasingly being used in hiring to screen. This is to out potential employees who have a higher likelihood of agitating for increased wages or supporting unionisation. Employers are increasingly monitoring employees’ emails, chats, and data to assess which might leave and calculate the minimum pay increase to make them stay.
None of these examples should be surprising, Newman argued. They are simply a modern manifestation of what employers have historically done to suppress wages by targeting and breaking up union activities. The use of personality assessments in hiring, which dates back to the 1930s in the US, in fact began as a mechanism to weed out people most likely to become labor organizers. The tests became particularly popular in the 1960s and ’70s once organizational psychologists had refined them to assess workers for their union sympathies.
In this context, Sapia’s fight-risk assessment is just another example of this trend. “Job hopping, or the threat of job hopping,” points out Barocas, “is one of the main ways that workers are able to increase their income.” The company even built its assessment on personality screenings designed by organizational psychologists.
Barocas doesn’t necessarily advocate tossing out the tools altogether. He believes the goal of making hiring work better for everyone is a noble one and could be achieved if regulators mandate greater transparency.
By Karen Haoa, July 24, 2020, MIT Technology Review | https://www.technologyreview.com/
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Does diversity start with the way you hire?
To find out how to interpret bias in recruitment, we also have a great eBook on inclusive hiring.
In the last month alone, four well-known consumer brands, including Netflix and Hubspot, have published their glossy self-assessments of their progress against Diversity and Inclusion.
If you look at the hard data, though, there is still so little real progress despite so many initiatives and positive intent.
The recent Bersin report shows that, surprisingly, diversity in the U.S work context is going backwards.
That’s because it has to involve some discomfort that cannot be avoided.
One of the first things companies need to do to improve Diversity and Inclusion is to scrutinise their hiring and promotion process.
- Are you relying 100% on human judgement? Decades of research has proved we are biased, especially in hiring.
- Is there any objective data captured about the candidate to ensure a fairer and smarter decision? CV data does not count as objective data; neither does having five subjective viewpoints on a candidate from 5 different interviewees.
- Are you using screening tools or assessments like video interviews? These exclude large chunks of the candidate pool, i.e. introverts, people with autism, people of colour, people who don’t like playing games.
A recent post about the impact of non-inclusive hiring on a 21-year-old job-seeker attracted 35k views and countless efforts to help him find a job. 
It was great to see, but what about the other few million candidates out there who haven’t had the benefit of a social media post to get a job?
Unless we solve the root of the problem, the post achieved little.
Inclusiveness is often a journey, but the most immediate thing we can all control is removing bias from hiring and promotion.
This is literally something that you can start doing tomorrow. Addressing systemic bias is more complex but removing unconscious bias is something we can solve for now.
Get me out of here!
From a candidate’s perspective, applying for a job is demoralising and exclusive. It often involves playing a game, glamping up for a video interview, mind-numbing multi-choice questions, only to be ghosted 99% of the time.
We have been speaking about candidate-first for a decade, now is the time to do it.