Amazon AI debacle won’t stop adoption for recruitment

The sexist artificial intelligence recruitment debacle at Amazon should prove a blip as the technology gains ground in and for human resources departments

Amazon hit the headlines in October this year following revelations that the online shopping giant had scrapped the development of a secret artificial intelligence (AI)-based recruitment tool because it showed bias against women.

The project was intended to automate the sifting of job-seekers’ CVs by giving them a score ranging from one to five stars. But by 2015, a year after the initiative was launched, the Amazon team realised that candidates for software developer and other technical roles were not being dealt with in a gender-neutral way.

The problem was that the data fed into the AI system, which consisted of 10 years’ worth of CVs, reflected the male dominance of the tech industry. As a result, the system taught itself to prefer male candidates and penalised CVs that included words like “women’s” as in “women’s chess club captain”. It also downgraded applicants from two US all-female colleges.

Although Amazon initially tried to edit the software to make it respond neutrally to female-oriented terms, there was no guarantee that gender discrimination would not be introduced in other ways and so the project was shelved in early 2017.

While the retailer attested that the tool had never been used in a real-world scenario by its recruiters, the incident does raise a number of issues, not least whether recruitment will remain a jumping off point for AI to take root and grow across the wider human resources (HR) function, as has been the presumption to date.

So what lessons can be learned from Amazon’s unfortunate experience and what kind of impact is it likely to have on adoption levels in an evolving market such as this one?

Jozsef Blasko, senior HR director at the Coca Cola Company, believes that, based on his research at the Department of Management at  the London School of Economics into hiring talent for team capacity building which included AI usage, the Amazon scenario should be taken as a “warning sign”.

The issue is that because machine learning software can only learn based on the data that is input into it, so any pre-existing bias will inevitably be baked into the system and possibly even amplified, he says.

Nonetheless, although Blasko thinks the situation could temporarily “influence the market and the mood”, ultimately he does not view it as a “deal breaker”. Instead, he sees AI adoption as part of an ongoing trend towards standardising the recruitment process both to make it more repeatable and to improve the candidate experience.

An evolving market

Meanwhile, according to Doug Rode, senior managing director at recruitment consultancy Michael Page, which is already using AI to automate some of its own hiring processes, while it is true that the market is still maturing, some early adopters are now starting to use it in what he describes as both “basic and sophisticated” ways.

At a basic level, the software is being employed to streamline and automate workflow processes in areas such as CV screening and processing as well as arranging interviews. But more sophisticated applications are also being used to undertake activities such as candidate assessment.

“One of the key drivers behind the adoption of AI software in the recruitment space is the time-saving benefits it offers users,” says Rode. “It can also help to streamline lengthy processes and is particularly useful when applied to roles that generate a large volume of applications such as sales or customer service.

“In order to work properly, this sort of technology needs a lot of data, which can take time to collate and feed into. AI technology becomes more intelligent and useful over time, so it’s important for businesses to have realistic expectations and understand that it isn’t a quick fix.”

AI technology becomes more intelligent and useful over time, so it’s important for businesses to have realistic expectations and understand that it isn’t a quick fix
Doug Rode, Michael Page

Another important consideration, says Marc Stuut, head of global talent acquisition at Unit4 Business Software, is to ensure there is a well-defined strategic objective and clear return on investment (ROI) before jumping in.

“The question is whether a particular type of application is validated and reliable, and what kind of investment does it require in terms of purchasing additional tools and software? It can also be hard to determine the ROI as there aren’t that many proof points, or proven business cases, around at the moment,” he says.

But despite the promise that AI software holds in terms of cutting costs and improving efficiency, Michael Page’s Rode does not expect to it to completely replace humans by automating the entire recruitment process from end-to-end.

“Positive candidate experiences often come from the productive and meaningful human connections formed during the recruitment process, so completely automating this and relying on AI technology would be detrimental to these relationships,” he says. “Therefore, AI should not be seen as replacing human interaction, but complementing it.”

Coca Cola’s Blasko agrees. “As long as team leaders have objectives and responsibility for delivering on those objectives, they’ll want to remain involved in the hiring process as they have a stake in an individual’s success or failure,” he says. “So while machines may make recommendations about selection, it won’t be more than that and humans will always make the final call.”

Case study: Heat Recruitment

Heat Recruitment, which uses AI functionality across a number of different hiring processes, sees the software less as a means of replacing staff and more about enhancing what they do.

The technology, which is embedded into the consultancy’s candidate database, customer relationship management system and various add-on modules, makes it possible to automatically match job-seekers with available roles to create possible shortlists rather than rely on faulty human memory to identify candidates. It can also send them automatic, personalised messages as required.

So that job-seekers do not become frustrated in having to undertake multiple searches for different equivalent job titles on the company’s website, the AI system is likewise able to recognise these discrepancies and include them all in its findings.

Another useful activity consists of identifying recruitment patterns and trends in the company’s data. For example, the software has revealed that hiring in the financial services space tends to slow down during March in anticipation of the financial year-end, making it easier to work out when and where staff should focus their efforts to be most productive.

Steve Preston, Heat’s managing director, says: “AI helps to remove those repetitive, basic tasks such as scheduling interviews that take a lot of time, which allows our consultants to focus on higher value activities. It also makes their jobs more enjoyable as the less interesting tasks are taken over by AI, so their wellbeing is better and they’re happier.”

Since starting to use software two years ago, Preston indicates that retention rates have jumped from 84% to 93% among consultants who have passed their probationary period. Two out of three now also stay on after going through their training, compared with the previous one in three.

Preston attributes the doubling of the consultancy’s turnover and trebling of its headcount to 85 over that time directly to the introduction of AI. There has also been a three-fold jump in the number of candidates applying for jobs from its website.

But Preston does not believe there will ever come a time when AI will run the recruitment process from end-to-end, not least because it is unclear who the client would go to if things went wrong. he says.

“Fundamentally, recruitment is a relationship-based industry – relationships with work colleagues, candidates and clients – and you have to understand all of them,” he says. “So while AI will help with selection, the last phase of screening and the final decision will always be human.”

Case study: NBCUniversal

“It is vital that when we add AI into the recruiting effort, we don’t lose our human appeal and risk brand equity declines in the process,” says Seldric Blocker, global vice president and talent acquisition director for NBCUniversal’s early career programs.

NBCUniversal is a US-based multinational media and entertainment conglomerate that is owned by telecommunications giant, Comcast. It recognised that not only were ethnic minorities and women under-represented across its business, but unconscious bias was also an issue that made itself felt among people of all backgrounds and cultures.

The situation was further compounded by the fact that New York and Los Angeles are the country’s largest media hubs, but are generally considered expensive and inaccessible to move to for people from lower socio-economic backgrounds.

As a result, the company decided to introduce recruitment software provider Oleeo’s Intelligent Selection system in a bid to boost the diversity of its workforce. The aim in doing so, says Blocker, was to ensure that it consisted of people with “multiple points of view that reflect society’s multiple points of view”.

“That means being ever more inclusive of diverse audiences, developing content that matters and enhancing the diversity of our own talent,” he says. “AI provides innovative ways of making this a transparent part of our business plan with the next generation of talent.”

The system was introduced to support the hiring of “future leaders” as it was considered the most competitive area of recruitment globally. Moreover, Blocker says, it “was an area where we needed to make strides in delivering diversity and inclusion”.

The AI software itself, meanwhile, scores all applications and event registrations using more than 120 prescribed data points within job-seekers’ CVs and profiles. The total for each candidate is then aggregated to provide a final score, and this score is used to create a list of recommended candidates that best match the criteria set, all “in a fraction of the time it would take humans”, says Blocker.

To double-check that no bias has entered the system though, an explanation to clarify how scores were arrived at is also given during each element of the process.

In graduate recruitment terms, Blocker asserts that the system is now helping NBCUniversal make “significant strides” in meeting Comcast’s goals of having ethnic minorities consist of 33%, and women 50%, of its workforce in the future.

“When we let machines use data to make prescriptive recommendations, we are free to spend more time on the high touch, human side of talent acquisition – nurturing great relationships,” he concludes.

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