With so much focus on how machines will automate recruiting, one researcher argues we cannot overlook the importance of human psychology as a crucial machine learning tool.

Given the amount of sci-fi films released in the last three decades involving robot uprisings, it’s clear that humans often cower in the face of machines’ intellectual superiority. However, the crux of many of these movies is that humans often win against Artificial Intelligence (AI) thanks to our creative problem solving and empathy. We point to this distinction to reinforce the idea that human labor is more valuable than robotic automation, particularly for tasks that require emotional intelligence.

This logic applies to make the argument why recruiters are indispensable to the hiring process. Whereas machines are capable of carrying out defined tasks, humans can differentiate nuances between candidates, allowing them to determine the best fit for any given role. Nevertheless, this hasn’t stopped the rise of AI technology within talent acquisition.

Industrial psychologist Dr. Charles Handler believes that organizations will fully automate, and enhance, the applicant selection and screening process by combining deep learning AI with the science of psychology. Dr. Handler sees greater potential for machines and humans after combining forces. “While a computer can beat any human at chess 100 percent of the time,” he says, “a computer and a human playing together can beat even the most advanced chess computer.”

Experts like Dr. Handler believe that automated predictive hiring decisions will soon be able to achieve near 100 percent accuracy. This means that an AI assistant or chatbot will choose candidates with infinitesimal margins of error.

In theory, this could replace an entire recruiting staff and automate the majority of HR functions, leading to billions, if not trillions, in bottom-line savings. AI assessment software like SmartAssistant, Zoom.ai, Textio, and Ideal are already in use at many forward-thinking companies. What’s more, high-impact teams are 6x more likely to use AI, predictive analytics, and other tech solutions to make data-driven decisions over their lower-performing counterparts. Research indicates that top performers see 18 percent higher revenue and 30 percent greater profitability compared to those that don’t use these tools.

In some ways, current AI technology is reminiscent of the droids in Star Wars: Attack of the Clones. Much like the simple robots themselves, basic AI can complete programmatic tasks like keyword or image recognition, but fall short when facing certain, more complex operations. The droids have no problem volleying lasers in gigantic battles, but once an enemy gets up close, these droids struggle to hit the broadside of a barn.

Currently, recruiting AI can scan a resume to identify patterns and compare against pre-programmed “high-performing employee” profiles. The model then predicts if the candidate will be successful based on the amount of matching criteria. Today’s AI models are still leagues away from watching and evaluating candidates through video interviews or more traditional face-to-face interactions.

Dr. Handler believes that these shortcomings could be solved if engineers were able to develop deep learning AI with an advanced understanding of human psychology. He argues that machines currently follow guidelines much more efficiently than humans, but cannot make value judgements about their decisions. By somehow “teaching” machines elements of human psychology (i.e. empathy) and assigning meaning to their behaviors, automated processes like candidate selection would look, and feel, more human.

“For hire-bots to be able to do their job as well, or better, than humans, they are going to have to understand individual differences the same way that humans do,” says Dr. Handler. “In other words, to be truly game-changing, hiring assessment AIs are going to have to think like psychologists.”

In this sense, “thinking like a psychologist” means the bots must base their analysis on psychometrics, clinical measurements of knowledge, abilities, attitudes, and personality traits that determine an individual’s true self. Hypothetically, by combining this measurement criteria with programmed professional judgment, an AI model could spot individual differences between candidate profiles without ever looking at resumes and decide which candidate would be the best match for an open position.

Counter to these utopian ideas are ethical arguments criticizing recruitment AI tech. One argument warns how genetic data could factor into hiring decisions, introducing a previously unfathomable bias. Another fear from critics is the idea of AI “cyber-snooping” on social media to create potential candidate profiles for a position. Despite these concerns, Dr. Handler appears confident in the future of recruitment AI.

“There is a lot to be gained by using AI to help psychologists to better understand individual differences,” he argues. “So, when creating the hire bots of tomorrow, don’t forget to include good old-fashioned psychology into the mix.”

This ecommerce giant made its billions by automating shopping service, but when it came to recruiting it made three crucial mistakes that lead to bias.

According to sources close to the project, it was obvious from the first year that AMZN.O – Amazon’s Recruiting AI – did not like women… like, at all!

The classified project quietly started in 2014, the Seattle company sought to create in-house computer programs to review and score candidates, sources told Reuters. “Everyone wanted this Holy Grail,” one source shared. “They literally wanted it to be an engine where I’m going to give you 100 resumes, it will spit out the top five, and we’ll hire those.”

In much the same way that customers rate products, AMZN.O rated candidates from one to five stars. However, a year into the experiment a gender bias became apparent, especially for software developers and technical posts.

The problem was in the data – the algorithm was feeding on a decade of almost all male resumes and concluding that the ideal candidate is a man, or rather, not a woman. AMZN.O would dock points from graduates of all women’s colleges and downgrade resumes with the word woman/women like “women’s chess club captain.”

Sources say that the algorithm was edited to be neutral to these specific terms, but there was still the fear that the program would teach itself new ways to detect femme resumes and continue to grade them lower.

And though Amazon owes most of its success to its ability to automate everything from warehouse management to pricing, the project was scrapped last year as executives lost hope that AMZN.O could ever be functional. The sources, who only agreed to speak with Reuters a year after the project ended — and under complete anonymity — maintain that no hiring decisions were made using the bias AI.

For some, this story is proof that we aren’t ready for AI in recruiting, and indeed there is still much to learn. Computer scientists like Nihar Shah, who teaches machine learning at Carnegie Mellon University, warn that an algorithm is easier to make than to control.

“How to ensure that the algorithm is fair, how to make sure the algorithm is really interpretable and explainable – that’s still quite far off.”

However, according to 2017 CareerBuilder survey, 55 percent of US HR managers said that AI will be a regular part of their work within the next five years. So is the solution really to avoid artificial intelligence in recruiting all together, or are there some lessons we can glean from this AI debacle? For further insight, we talked to the director of product for SmartRecruiters, Hessam Lavi.

“Developers of this type of systems have an enormous responsibility to prevent negative biases to shape the artificial intelligence they want to produce,” says Lavi. “So, proper training needs to take place to learn not just the technical and process effects of artificial intelligence, but how AI will affect natural beings as well.”

Lavi, who recently headed the team in building SmartAssistant, the first recruiting AI native to an ATS, sees three crucial mistakes when it comes to AMZN.O

1. Thinking the bias is coming from the machine: Negative biases are unfortunately part of the recruiting trade, whether from humans or machines – only it’s much harder to detect in people. So, having a system that makes biases apparent is valuable in itself. The AI learns from the data you feed it, so it’s not the program that’s biased so much as the people who made the decisions that the computer is now analyzing. Eliminating the program is not tantamount to eliminating bias.
2. Limiting the data set: The dataset from one company, even one as big as Amazon, just isn’t enough. A singular company may be using bias paradigms unintentionally. The bottom line is, the more data the better.
3. Deriving future predictions from past events: Past-predicts-future AI can work great for domains such as medical imaging that have a very narrow focus, for example, forecasting the growth of a tumor where an AI can be trained to make clear-cut decisions and act as an expert. However, in hiring which involves a wide range of factors, this type of assumptive AI tends to emphasize biases of the past. If you only had men hired in the past, the algorithm may assume it’s because they are the best people for the job and will continue prioritizing them for future positions.

His best advice? Avoid the black box!

“When we built SmartAssistant we split up the decision processing into smaller, distinct components,” says Lavi. “For example, one component would analyze candidates’ industry experience, one would examine education, one would evaluate soft skills, and so on. Through creating these stand-alone units, we can trace negative outcomes back to their origin and understand why they are happening.”

“We believe the final decision in the recruiting process will be made by humans for the foreseeable future,” Lavi affirms. “But, AI has the ability to automate many of the repetitive tasks and winnow down the stacks of resumes that overwhelm recruiters and cause them to lean on their negative biases. AI technology is much more than just automating tasks and it can teach us about how we make decisions and point out shortcomings in our abilities.”

If you want to stay ahead in the hiring game, there are some tech advances you need to be aware of, or even more, to master.

There’s a large skills gap in today’s job market, and hiring professionals are finding it difficult to find the right candidates in this highly competitive market. This has given rise to hiring managers leaning more on social recruiting, artificial intelligence products, and overall HR technology upgrades. Here are four ways HR Tech will take your talent acquisition process to the next level.

1. Implementing Artificial Intelligence/Chatbots

Using artificial intelligence or chatbots in your recruiting process can drastically reduce tedium. Reviewing resumes, scheduling interviews and internal coordination are all necessary, but with a chatbot, all these tasks can be automated, leaving HR and recruitment professionals to focus on more complex activities such as employer branding, improving outreach, and other related tasks.

2. Posting on Multiple Job Boards

Recruiting professionals are relying on job boards to source quality talent. According to Zety, about 52% of applications arrive via from job boards. Most applicant tracking systems (ATS) offer job board integrations. Posting to several job boards at once is a great way to get more exposure, and with an ATS, your hiring team can save hundreds of work hours otherwise lost to filtering through a pool of applicants.

3. Pre-screening Candidates

AI for recruiting is designed to reduce or remove time-consuming activities like manually screening resumes. 52% of Talent Acquisition leaders say the hardest part of recruitment is screening candidates from a large applicant pool. AI screening software adds functionality to the ATS by using hiring data like performance and turnover to make hiring recommendations for new applicants. The recommendations are made by applying learned information about existing employees’ skills to automatically grade new candidates.

4. Using AI to Remove Bias From Recruitment Process

AI is able to assess data points around successful employees and objectively determine the best hires in a talent pool. Recruiting AI can be programmed to ignore demographic information, such as gender, race, and age to increase the opportunity for diversity.

AI also exposes bias in your recruiting, giving you a clear view of potential problems and the ability to address them.

If you’re thinking about improving your talent acquisition process, start with a 30 day free trial of XOR, which will streamline your process, lower your cost-per-hire by 50% and decrease your time-to-fill by 33%.