We hope that the debate over the value of diverse teams is now over. There is plenty of evidence that diverse teams lead to better decisions and therefore, business outcomes for any organisation.
This means that CHROs today are being charged with interrupting the bias in their people decisions and expected to manage bias as closely as the CFO manages the financials.
But the use of Ai tools in hiring and promotion requires careful consideration to ensure the technology does not inadvertently introduce bias or amplify any existing biases.
To assist HR decision-makers to navigate these decisions confidently, we invite you to consider these 8 critical questions when selecting your Ai technology.
You will find not only the key questions to ask when testing the tools but why these are critical questions to ask and how to differentiate between the answers you are given.
Another way to ask this is: what data do you use to assess someone’s fit for a role?
First up- why is this an important question to ask …
Machine-learning algorithms use statistics to find and apply patterns in data. Data can be anything that can be measured or recorded, e.g. numbers, words, images, clicks etc. If it can be digitally stored, it can be fed into a machine-
The process is quite basic: find the pattern, apply the pattern.
This is why the data you use to build a predictive model, called training data, is so critical to understand.
In HR, the kinds of data that could be used to build predictive models for hiring and promotion are:
If you consider the range of data that can be used in training data, not all data sources are equal, and on its surface, you can certainly see how some carry the risk of amplifying existing bases and the risk of alienating your candidates.
Using data that is invisible to the candidate may impact your employer brand. And relying on behavioural data such as how quickly a candidate completes the assessment, social data or any data that is invisible to the candidate might expose you to not only brand risk but also a legal risk. Will your candidates trust an assessment that uses data that is invisible to them, scraped about them or which can’t be readily explained?
Increasingly companies are measuring the business cost from poor hiring processes that contribute to customer churn. 65% of candidates with a positive experience would be a customer again even if they were not hired and 81% will share their positive experience with family, friends and peers (Source: Talent Board).
Visibility of the data used to generate recommendations is also linked to explainability which is a common attribute now demanded by both governments and organisations in the responsible use of Ai.
Or that they perpetuate societal biases and could end up penalising nonnative speakers, visibly nervous interviewees or anyone else who doesn’t fit the model for look and speech.
If you are keen to attract and retain applicants through your recruitment pipeline, you may also care about how explainable and trustworthy your assessment is. When the candidate can see the data that is used about them and knows that only the data they consent to give is being used, they may be more likely to apply and complete the process. Think about how your own trust in a recruitment process could be affected by different assessment types.
1st party data is data such as the interview responses written by a candidate to answer an interview question. It is given openly, consensually and knowingly. There is full awareness about what this data is going to be used for and it’s typically data that is gathered for that reason only.
3rd party data is data that is drawn from or acquired through public sources about a candidate such as their Twitter profile. It could be your social media profile. It is data that is not created for the specific use case of interviewing for a job, but which is scraped and extracted and applied for a different purpose. It is self-evident that an Ai tool that combines visible data and 1st party data is likely to be both more accurate in the application for recruitment and have outcomes more likely to be trusted by the candidate and the recruiter.
At PredictiveHire, we are committed to building ethical and engaging assessments. This is why we have taken the path of a text chat with no time pressure. We allow candidates to take their own time, reflect and submit answers in text format.
We strictly do not use any information other than the candidate responses to the interview questions (i.e. fairness through unawareness – algorithm knows nothing about sensitive attributes).
For example, no explicit use of race, age, name, location etc, candidate behavioural data such as how long they take to complete, how fast they type, how many corrections they make, information scraped from the internet etc. While these signals may carry information, we do not use any such data.
Another way to ask this is – Can you explain how your algorithm works? and does your solution use deep learning models?
This is an interesting question especially given that we humans typically obfuscate our reasons for rejecting a candidate behind the catch-all explanation of “Susie was not a cultural fit”.
For some reason, we humans have a higher-order need and expectation to unpack how an algorithm arrived at a recommendation. Perhaps because there is not much to say to a phone call that tells you were rejected for cultural fit.
This is probably the most important aspect to consider, especially if you are the change leader in this area. It is fair to expect that if an algorithm affects someone’s life, you need to see how that algorithm works.
Transparency and explainability are fundamental ingredients of trust, and there is plenty of research to show that high trust relationships create the most productive relationships and cultures.
This is also one substantial benefit of using AI at the top of the funnel to screen candidates. Subject to what kind of Ai you use, it enables you to explain why a candidate was screened in or out.
This means recruitment decisions become consistent and fairer with AI screening tools.
But if Ai solutions are not clear why some inputs (called “features” in machine learning jargon) are used and how they contribute to the outcome, explainability becomes impossible.
For example, when deep learning models are used, you are sacrificing explainability for accuracy. Because no one can explain how a particular data feature contributed to the recommendation. This can further erode candidate trust and impact your brand.
The most important thing is that you know what data is being used and then ultimately, it’s your choice as to whether you feel comfortable to explain the algorithm’s recommendations to both your people and the candidate.
Assessment should be underpinned by validated scientific methods and like all science, the proof is in the research that underpins that methodology.
This raises another question for anyone looking to rely on AI tools for human decision making – where is the published and peer-reviewed research that ensures you can have confidence that a) it works and b) it’s fair.
This is an important question given the novelty of AI methods and the pace at which they advance.
At PredictiveHire, we have published our research to ensure that anyone can investigate for themselves the science that underpins our AI solution.
We continuously analyse the data used to train models for latent patterns that reveal insights for our customers as well as inform us of improving the outcomes.
It’s probably self-evident why this is an important question to ask. You can’t have much confidence in the algorithm being fair for your candidates if no one is testing that regularly.
Many assessments report on studies they have conducted on testing for bias. While this is useful, it does not guarantee that the assessment may not demonstrate biases in new candidate cohorts it’s applied on.
The notion of “data drift” discussed in machine learning highlights how changing patterns in data can cause models to behave differently than expected, especially when the new data is significantly different from the training data.
Therefore on-going monitoring of models is critical in identifying and mitigating risks of bias.
Potential biases in data can be tested for and measured.
These include all assumed biases such as between gender and race groups that can be added to a suite of tests. These tests can be extended to include other groups of interest where those group attributes are available like English As Second Language (EASL) users.
On bias testing, look out for at least these 3 tests and ask to see the tech manual and an example bias testing report.
At PredictiveHire, we conduct all the above tests. We conduct statistical tests to check for significant differences between groups of feature values, model outcomes and recommendations. Tests such as t-tests, effect sizes, ANOVA, 4/5th, Chi-Squared etc. are used for this. We consider this standard practice.
We go beyond the above standard proportional and distribution tests on fairness and adhere to stricter fairness considerations, especially at the model training stage on the error rates. These include following guidelines set by IBM’s AI Fairness 360 Open Source Toolkit. Reference: https://aif360.mybluemix.net/) and the Aequitas project at the Centre for Data Science and Public Policy at the University of Chicago
We continuously analyse the data used to train models for latent patterns that reveal insights for our customers as well as inform us of improving the outcomes.
We all know that despite best intentions, we cannot be trained out of our biases. Especially the unconscious biases.
This is another reason why using data-driven methods to screen candidates is fairer than using humans.
Biases can occur in many different forms. Algorithms and Ai learn according to the profile of the data we feed it. If the data it learns from is taken from a CV, it’s only going to amplify our existing biases. Only clean data, like the answers to specific job-related questions, can give us a true bias-free outcome.
If any biases are discovered, the vendor should be able to investigate and highlight the cause of the bias (e.g. a feature or definition of fitness) and take corrective measure to mitigate it.
If you care about inclusivity, then you want every candidate to have an equal and fair opportunity at participating in the recruitment process.
This means taking account of minority groups such as those with autism, dyslexia and English as a second language (EASL), as well as the obvious need to ensure the approach is inclusive for different ethnic groups, ages and genders.
At PredictiveHire, we test the algorithms for bias on gender and race. Tests can be conducted for almost any group in which the customer is interested. For example, we run tests on “English As a Second Language” (EASL) vs. native speakers.
If one motivation for you introducing Ai tools to your recruitment process is to deliver more diverse hiring outcomes, it’s natural you should expect the provider to have demonstrated this kind of impact in its customers.
If you don’t measure it, you probably won’t improve it. At PredictiveHire, we provide you with tools to measure equality. Multiple dimensions are measured through the pipeline from those who applied, were recommended and then who was ultimately hired.
8. What is the composition of the team building this technology?
Thankfully, HR decision-makers are much more aware of how human bias can creep into technology design. Think of how the dominance of one trait in the human designers and builders have created an inadvertent unfair outcome.
In 2012, YouTube noticed something odd.
When designers investigated the problem, they found something unexpected: Left-handed people picked up their phones differently, rotating them 180 degrees, which lead to upside-down videos being uploaded,
The issue here was a lack of diversity in the design process. The engineers and designers who created the YouTube app were all right-handed, and none had considered that some people might pick up their phones differently.
In our team at PredictiveHire, from the top down, we look for diversity in its broadest definition.
Gender, race, age, education, immigrant vs native-born, personality traits, work experience. It all adds up to ensure that we minimise our collective blind spots and create a candidate and user experience that works for the greatest number of people and minimises bias.
What other questions have you used to validate the fairness and integrity of the Ai tools you have selected to augment your hiring and promotion processes?
We’d love to know!
Predictive Talent Analytics turns the imaginary into reality, presenting a variety of businesses, including contact centres, with the opportunity to improve hiring outcomes and raise the performance bar. With only a minor tweak to existing business processes, predictive talent analytics addresses challenge faced by many contact centres.
Recruitment typically involves face-to-face or telephone interviews and psychometric or situational awareness tests. However, there’s an opportunity to make better hires and to achieve better outcomes through the use of Predictive Talent Analytics.
Many organisations are already using analytics to help with their talent processes. Crucially, these are descriptive analytical tools. They’re reporting the past and present. They aren’t looking forward to tomorrow and that’s key. If the business is moving forward your talent tools should also be pointing in the same direction.
Consider a call-waiting display board showing missed and waiting calls. This is reporting.
Alternatively, consider a board that does the same but also accurately predicts significant increases in call volumes, providing you with enough time to increase staffing levels appropriately. That’s predictive.
Descriptive analytical tools showing the path to achievement taken by good performers within the business can add value. But does that mean that every candidate within a bracketed level of academic achievement, from a particular socio-economic background, from a certain area of town or from a particular job board is right for your business? It’s unlikely! Psychometric tests add value but does that mean that everyone within a pre-set number of personality types will be a good fit for your business? That’s also unlikely.
The simple truth is that, even with psychometric testing and rigorous interviews, people are still cycling out of contact centres and the same business challenges remain.
With only a minor tweak to talent processes, predictive talent analytics presents an opportunity to harness existing data and drive the business forward by making hiring recommendations based on somebody’s future capability.
But wait, it gets better!
Pick the right predictive talent analytics tool and this can be done in an interesting, innovative and intriguing way taking approximately five minutes.
Once the tool’s algorithm knows what good looks like, crucially within your business (because every company is different!), your talent acquisition team can approach the wider talent market armed with a new tool that will drive up efficiency and performance.
Picking the right hires, first time.
Consider this. Candidate A has solid, recent, relevant experience and good academic grades, ticking all the right hiring boxes but post-hire subsequently cycles out of the business in a few months.
Candidate B is a recent school-leaver with poor grades, no work history but receives a high-performance prediction and, once trained, becomes an excellent employee for many years to come.
On paper candidate A is the better prospect but with the fullness of time, candidate B, identified using predictive talent analytics, is the better hire.
Instead of using generic personality bandings to make hiring decisions, use a different solution.
Use predictive talent analytics to rapidly identify people who will generate more sales or any other measured output. Find those who will be absent less or those who will help the business achieve a higher NPS. Bring applicants into the recruitment pipeline knowing the data is showing they will be a capable, or excellent, performer for your business.
Now that’s an opportunity worth grasping!
Steven John worked within contact centres whilst studying at university, was a recruiter for 13 years and is now Business Development Manager at Sapia, a leading workforce science business providing a data-driven prediction with every hire. This article was originally written for the UK Contact Centre Forum
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 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:
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.
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.
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.
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.
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.”
‘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.
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.
During this seasonal holiday a great many of us will start to create plans for the forthcoming New Year. We’ll think about events, occurrences and happenings of the year gone by and many will commit to doing things better next year.
Even though studies have shown that only 8% of people keep their New Year’s resolutions , we still make (and subsequently break) them. But the intention was there, so good work!
Have you ever stopped to think about the processes your brain undertakes to enable you to set your goals for the New Year? No? Well, luckily I’ve done that bit for you. To make that resolution you combined your current and historical personal data and produced a future outcome, factoring in the probability of success, based on your analysis. A form of predictive analytics, if you like!
Thinking about those things you did (and didn’t do) this year and predicting/projecting for next year.
So now you know what it involves and we are (loosely) agreed that you’re on board with predictive analytics, when better than to tell you now that 2016 is going to be the year when we really start to see the benefits of predictive analytics within our jobs and people functions at work.
I think it’s now universally accepted that when technology is used in the right way it can enhance and improve our lives across every sector and industry. Most fields have seen significant developments over the last 20-30 years as technology is increasingly used to further our understanding of the way things work, enabling us to make better decisions in areas such as medicine, sport, communication and, arguably, even dating (predictive analytics is used in all of those sectors by the way!) so why not use it to help us find the right people for the right organisations?
Did you know you no longer need a top-class honours degree to work at Google?
Every employee is put through their analytics process allowing the business to match the right person with the right team, giving each individual the best environment to allow their talent to flourish.
Companies such as E&Y and Deloitte are using different methods to tackle the same problem – removing conscious and subconscious bias attached to the name and/or perceived quality of the university where applicants studied.
Airlines, retail, BPOs, recruitment firms a growing number of businesses within these sectors are using or on-boarding predictive analytics to achieve upturns in profits, productivity and achieving a more diverse and happier workforce.
Predictive analytics helps us make people and talent decisions to positively influence tomorrow’s business performance without bias, so I guess the question is this – if it’s already a proven science to achieve results, why isn’t everyone doing it? How long until everyone starts to use, and see the benefits, of predictive analytics?
Data can be big and it can be daunting, but it can also be invaluable if you ask and frame the right questions and combine the answers with human knowledge and experience. You will be surprised by the insights, knowledge and benefits that your business can obtain from even the smallest amounts of data. Data you probably already collect, even if it’s unknowingly or unwittingly!
So as you start rummaging through your brain trying to remember where you filed your finest seasonal outfit(s) (that might just be me!), start prepping for the new year budgets, or start writing your list of resolutions let me help you frame a few questions:
Statistically, your personal New Year’s resolution is unlikely to be on course in 12-months time so instead, why not make a resolution to bring predictive analytics into your talent processes in the upcoming year?
You’ll see the benefits for years to come, and that’s a promise we can both keep.