The working world is going through a rapid transformation. The number of companies able to visibly compete for talent is increasing daily. The great resignation proves that a big brand and paycheque are no longer enough to retain good people, especially those in high demand.
Having a diverse workforce has been proven time and time again to give an organisation the edge. Winning clients, attracting talent or securing funding to take a company public can depend on diversity. For example, Goldman Sachs Group recently announced it would no longer support initial public offerings of companies with all-male boards. The returns on diversity are tangible, especially regarding innovation: a survey of 1,700 companies across eight countries found that organisations with above-average diversity had, on average, 19 percentage points higher innovation revenues and 9 percentage points higher earnings before interest and tax margins.
However, humans are inclined to bias, whether intentional or not. Organisations are therefore looking to block those inclinations to make objective decisions regarding hiring, firing, promoting and developing workers. We've been busy building the first generation of Clu over the past 12 months and have learnt a lot about the future of AI in recruitment and diversity recruitment. We thought we'd share some of our findings with you.
So long CV-sifting AI
The traditional process of scanning and tossing out resumes is clearly riddled with implicit bias. According to one intensive academic study, minority applicants who 'whitened' their resumes were more than twice as likely to receive calls for interviews, and it did not matter whether the organisation claimed to value diversity or not. Also, Joe vs Jose from BuzzFeed is a great quick demonstration of this.
Many vendors have touted AI to overcome bias in resume parsing, but there are two problems with this. First, sorting resumes more efficiently will not move the needle on diversity and inclusion (D&I) if there aren't enough people from various backgrounds in the candidate pool. Second, AI-fuelled resume sorting applications can make existing biases worse. Consider Amazon's experimental recruiting engine. Having learned from the training data that male candidates had more experience in technical roles, the application started penalising resumes that included the word 'women's' as in 'women's volleyball.' (Amazon did wisely scrap the project, by the way.)
So not even AI can save the resume, and algorithms are no panacea for D&I. But when the technology is applied within a thoughtful context, it can be a powerful tool to help level the playing field for people who have traditionally been overlooked and those who haven't been taught how to write a winning CV and cover letter.
Diversity only increases through holistic inclusive practice
Using bias-mitigating technology for candidate hiring and assessment is only part of the solution for attracting and maintaining a diverse workforce. However, ramping up efforts to increase the representation of certain groups will all be for nought if these individuals are having a poor experience after they join the organisation. This is why we also believe that the name-blind CV process will soon become a thing of the past as removing people's identities ceases and organisational accountability and transparency increase.
Only 35% of CDOs are tracking diversity data, according to a recent report delivered by a leading executive search firm. If an organisation is not tracking this data or hiding it in the application process, it will be missing important markers that will help determine whether people are being exposed to fair practices.
This is one example of when technology is incorporated into traditional disciplines like economics, organisations can build the kind of employment market where all talented people have an opportunity to thrive, regardless of their background.
AI unearths candidates with true potential
AI can be used to support the recruitment process by analysing the soft and technical skills of existing employees and informing talent teams where gaps are. Subsequently, AI could be then be used to fill in the gaps, thereby creating a more balanced and cognitively diverse workplace.
Being upfront and determining what characteristics are required to succeed in a certain role allows companies to massively evolve their recruitment strategy, enabling them to make far more informed decisions about a candidate over and above gut feel of whether they're the right fit or not. This is why we've built CLU's service around displaying what candidates can do, not what they've done to qualify them into processes. This helps to unearth the true potential in a candidate and eliminates the risk of hiring based on relationship, or prior experience.
Pair AI with human expertise for transparency
With AI expected to create $13 trillion in value for businesses by 2030, there is a clear need to create a universally integrated job-matching tool powered by AI to save businesses billions in failed recruitment.
The biggest oversight in recruitment AI and recruitment technology assumes candidates and organisations know what they're looking for. The focus on breadth over accuracy is deepening the deficit of trust between talent and organisations, with larger organisations with the most expensive recruitment technology solutions being stung the most.
While other industries are rapidly adopting AI and machine learning, recruitment is clinging rigidly to the old, profligate ways of working that benefit neither job seekers nor businesses. Our vision for Clu is to increase the accuracy of job searches dramatically and, in doing so, save businesses huge amounts of time and expenditure. A client can save more than '225,000 and over 600 man-hours when compared to traditional methods of recruiting.
Social interaction is going to remain the one area where humans will trump robots for many years to come. Integrating bias-mitigation features to safeguard diverse talent in recruitment processes is also key. Workplace culture, diversity and inclusion are growing in importance, which is why it is more important than ever not to try and use technology to replace people but use the best parts of both to complement and hold each other accountable.
To avoid biased output from AI tools and predictive analytics, when it comes to dealing with hiring for diversity and inclusion, it is vital that the data collected is reviewed thoroughly to identify and remove any biases before using it. Also, samples used to train the machines should always be trialled and modelled regularly, and if need be, algorithms adjusted accordingly by experts. Can we learn to create more ethical machines? If we get the balance right, then absolutely.