Written by Nathan Hewitt
How can we make hiring more inclusive for people with disabilities?
The Americans with Disabilities Act (ADA) passed in 1990. This year, Australia’s Disability Discrimination Act turned 30. Even after all that time, bias and discrimination against candidates and employees with disabilities continues to be an important topic.
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). Coupled with increased laws and regulations regarding the protection of disabled job applicants and employees (e.g., U.S. EEOC, 2022), it is no surprise that academics, employers, and selection vendors are keen to understand where potential disability bias exists so it can be reduced or, ideally, eliminated.
Traditional face-to-face interviews are a large entry barrier for people with disabilities
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). One study found that fake accountant job applicants that had disclosed a disability were 26% less likely to receive employment interest from the employer than those with no disability. Worse, experienced candidates with disabilities were 34% less likely to receive interest, despite presenting equally high levels of qualifications (Ameri et al., 2015). In addition to the bias held by hiring managers or recruiters, another concern is that certain selection methods create a very poor candidate experience for individuals with disabilities, causing them stress or anxiety and therefore stopping them from putting their best foot forward. For individuals with Autism Spectrum Disorder (ASD) in particular, in-person or video interviews can be very stressful, with less than 10% believing they are given the opportunity to demonstrate their skills and abilities in this process (Cooper & Kennady, 2021).
Stuttering is another form of disability where traditional in-person and video interviews where the candidate has to speak may lead to stress and anxiety (Manning and Beck, 2013). One study found that people who stutter find their stuttering to be a “major handicap” in their working lives and over 70% thought that they had a decreased opportunity to be hired and promoted (Klein & Hood, 2004). Other disabilities, such as dyslexia and other learning and language disabilities may cause candidates to struggle with timed online selection assessments, so it is important to identify and remove these barriers (Hyland & Rutigliano, 2013).
How do we better accommodate people with disabilities or neurodiversity in the way we interview and hire?
Cooper and Mujtaba (2022) recommend alternative approaches that allow candidates with ASD to showcase their skills without having to verbally communicate them or properly interpret nonverbal cues.
The use of an online, untimed, chat-based interview – that is, our Ai Smart Interviewer – 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 the candidate to express themselves freely and adequately demonstrate their skills.
The power of a Smart Interviewer, supported by research
Our Sapia Labs data science team has submitted a paper on reducing bias for people with disabilities to SIOP for 2023.
In the study, the data comes directly from our Smart Interviewer, which, as we said above, is an online untimed chat-based interview platform.
Candidates can give feedback after the interview process, and some candidates include self-report disability conditions in their feedback. While a number of different disabilities were mentioned, we had sufficient sample sizes to examine candidates with autism, dyslexia, and stutter. We compared their machine learning-generated final interview scores and yes/maybe/no hiring recommendations to a randomly sampled, demographically similar group of candidates that did not disclose a disability.
Effect sizes, 4/5ths ratios, and Z-tests revealed no adverse impact against candidates with autism, stutter or dyslexia. Additionally, feedback from these groups tended to indicate the experience was positive and allowed them the opportunity to do their best.
- “It was an unusual experience but as an autistic person, I appreciated being able to interview via text rather than phone. It gave me the chance to really consider my responses in my own time.”
- “I must admit this is much more relieving than a face-to-face interview as I fear that I would stutter and accidentally say something stupid.”
- “Being dyslexic, this interview gives me a fantastic opportunity to think and re-read my responses before delivery.”
True diversity and inclusion starts with the way you hire. Our Ai Smart Interviewer allows people with disabilities and neurodiversity – real people, with real ambitions – to represent themselves fairly.
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The AI model can predict whether you are a job seeker
The voluntary resignation of employees has a direct financial impact on the organisation. Moreover, when the pandemic broke out, most organisations were seeking to cut staff costs, and voluntary employee resignation would cause great concern to the company. Therefore, the ability to predict employee turnover rates can not only help make smart hiring decisions but also help you save a lot of financial crises in an uncertain time.
Correlation Between ‘Job Hopping’ & ‘Openness’
Recognising that researchers and data scientists from AI recruitment startup Sapia have built a language model that can analyze candidates’ open interview questions and infer the possibility of candidates changing jobs. The study, led by Madhura Jayaratne and Buddhi Jayatilleke, was conducted on the responses of 45,000 job applicants. Chatbots were used to conduct interviews and self-assess their likelihood of job-hopping.
The researchers evaluated five different text representation methods-term frequency-inverse document frequency (TF-IDF) abbreviation, LDS, GloVe Vectors for word representation, Doc2Vec file embedding, and language query and word count (LIWC). However, the GloVe embedding provides the best results. Thus, highlighting the positive correlation between the word sequence and the likelihood of an employee leaving.
The researchers further pointed out that there is also a positive correlation between employees’ job-hopping and their “open experience”. Because companies can provide the same forecasts for freshmen, companies that change careers can bring considerable financial benefits to the company.
The Real Cost of ‘Job Hopping’
In addition to the impact of new employees onboarding or outsourcing work to finances, increasing employee turnover rates may also reduce productivity and undermine employee morale. In fact, in this competitive landscape, the trend of leaving work in order to find a better job has received widespread attention. Therefore, it is critical for companies to assess the possibility of candidates for job-hopping before choosing.
Traditionally, this assessment is done by browsing the candidates’ resumes. However, manual intervention makes the process tiring and inaccurate. Additionally, this method is only suitable for professionals with work experience and is useless for novices and amateurs. Therefore, the researchers decided to use the interview answers to analyse the personality traits of candidates and their chances of voluntarily leaving.
Source: http://www.zhongguojinrongtouziwang.com/keji/202008/6876.html
Get the whitepaper here: Assessing Job-Hopping Attitudes From Chat Interview
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Myth or fact: Marginalised people are more likely to drop out when Ai tools are involved?
We get asked this a lot. It’s an important question, especially when it comes to creating a fair playing field among candidates looking for jobs.
There are two things that we do differently with our Ai technology that means our Ai is an improved experience for marginalised candidates.
Firstly, we use chat. Chat allows you to write in your own time, use your own words, and be happy with what you submit, when you are ready to submit it. It does not judge you visually, nor do our algorithms score you badly for typos, or having English as a second language.
Secondly, we use clean input data. Data from CVs carry inherent bias around gender, socio-economic standing, ethnicity, and age. It does not matter if the process is ‘blind’ (i.e. there is no name or gender attached to the CV), over time the machine will start to favour those that society has (largely white men). By using only data that is given in the interview through chat, our data is objective.
Chat doesn’t feel like an assessment, and it allows you to be interviewed in a familiar way. Think how different this is to platforms that gamify the recruitment process, creating a stressful and uncomfortable experience for many people. Chat creates a safe space to be yourself.
Recently, we undertook a piece of research for a large national retailer with regard to improving their experience for Aboriginal and Torres Strait Islander peoples, the Indigenous peoples of Australia. We took 2,454 applicants who self-identified as Indigenous Australians and compared this to a group that didn’t identify as such.
The analysis revealed that the retail group hired 1.2x people who identified as Indigenous Australians in their candidate pool. Candidate feedback rating was 9.37/10 (3% higher than non-Indigenous) highlighting the appeal of the platform.
Indigenous Australians have been overlooked for so long when it comes to jobs. When we gave them a fair chance and an experience that let them tell their story, something so inherent to their culture, they were elevated as candidates.
This is truly a profound outcome, and one we believe can change the lives of so many people traditionally overlooked for roles.
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What can HR learn from The Social Dilemma?
Objective data and truth.
Netflix’s latest documentary “The Social Dilemma” tells a story of data gone mad, of it being used to personalise ‘the truth’ so that everyone’s truth is their own. The idea of ‘objective truth’ doesn’t exist anymore.
The combination of hyper-connectivity at scale that comes from social media, the addictive habits of engaging with it, and the incredible ability to personalise what we see, listen to, and believe, creates a feeling of satisfaction at best (think Spotify) and at its worst, a fractured society.
So what’s the relevance of that for HR?
HR has been on this journey to do the opposite. To introduce an objective standard of truth – especially given the risks that come from personalised decision making when it comes to hiring. The risk of making hiring decisions based on personal views means we see hiring being influenced by unconscious biases – something that can be easier to identify than fix. ‘Mirror hiring’, and companies that hire for “culture-fit”.
Consider the decline of so many legacy Fortune 500 companies over the last 50 years.
Do you think Kodak and its ilk would have crashed as quickly if they had a genuinely diverse set of opinions and experiences at their leadership level?
It’s no coincidence that in The Social Dilemma most of the protagonists (if that’s the right word) sharing their regrets and insights on “how the heck did we get here?” were mostly young white men.
From my own experience of being HRD at a leading digital tech company, engineers were hired based on two data inputs: their coding ability, and their ‘fit’ with the team.
The former is readily tested using objective tools, but the latter is largely tested through having coffee chats with the team.
Or to put it another way – 100% subjective; 0% objective data.
Is it any wonder then that you end up with more of the same when you use the personal opinions of humans to drive these decisions? People are so scared of data amplifying bias, and humans can be pretty good at it too.
Bias in the recruiting process has been an issue for as long as modern-day hiring practices existed. In order to address some concerns, the idea of “blind applications” became popular a few years ago, with companies simply removing names on applications and thinking that it would remove any gender or racial profiling. It made a difference, but bias still existed though the schools that people attended, as well as past experience they might have had. Interestingly, these are two things that have now been shown to have no impact on a person’s ability to do a job.
Away from computer screens and smartphone addictions, when it comes to hiring, HR needs to do the very thing that social media has rendered mute.
It has to ensure that there is objective truth on every candidate. It has to do this for every new hire, every promotion.
Ironically, it is what social media weaponised – ‘data’ that can only, truly help us achieve this. I talk often about “objective data” – that is data that has been collected without input bias – and it is only this data that helps us disrupt bias that comes from putting humans in the decision in making seat. This objective data is more builds a truly holistic picture about an individual when helping inform hiring decisions, decisions that will shape a company’s culture, and its future.
(Read: Why Machines Make Better Decisions Than Humans Do)
The data seeks to understand who you are, not the school you went to, or the degree you hold, but how you think and behave and most of all your intrinsic traits. It was Facebook’s homogenous culture that encouraged technical brilliance over ethical thinking that ultimately created the issues discussed in The Social Dilemma. If only they’d used their skills to invest in objective data that set aside its technical bias and hired for humanity, we might not be questioning it in the way we are.
Here’s the next article in this series:
https://sapia.ai/blog/ai-serves-humans/
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You can try out Sapia’s Chat Interview right now, or leave us your details to get a personalised demo