Robots have already been put to work across a number of industries. They are manufacturing cars, taking care of the elderly, doing housework, homework, and even entering literary awards. It is not surprising then that new robots have been developed to conduct job interviews.

One such robot, Matlda, has been programmed to conduct 25-minute interviews in which she works through a roster of up to 76 questions. She records and analyses the interviewee’s responses, monitors facial expressions and compares them to other successful employees within the hiring company.

Matlda is not much taller than a wine bottle, but her introduction could set a new precedent for recruitment techniques across a range of sectors.

Doing so will affect the performance of interviewees differently. “Some candidates might present better in person and will be left worse off, but others may be more comfortable with a remote interview,” says Martin Ford, an expert on automation technology.

One particular demographic that might welcome chatting to Matlda is the post-millennial generation, also known as digital natives, who grew up swiping the pages of digital books, using self-checkout machines and asking Siri for directions. “We find that people often prefer to interact with something that’s not real; it’s all about reducing the cognitive load,” says Matthew Howard at King’s College London, whose students launched Kinba, a robotic receptionist at the university, earlier this year.

As with all forms of artificial intelligence, efficiency is a clear incentive. More important, though, are claims that artificial intelligence will help eliminate pre-existing prejudice within employment processes and boost transparency.

“Matlda’s mission is to be a service gateway for a more sustainable and humane society,” says Prof Rajiv Khosla at Melbourne’s La Trobe University, which developed the robot. “It’s a non-judgmental, non-threatening and non-invasive means of engaging people in uninhibited interaction.”

A level playing field?

The main advantage of involving computers, “is a consistency in decision making and the removal of some human error”, says Benedict O’Donovan, managing director at Durham Applied Robotics and Technologies. “While you might get two interviewers who interpret recruiting guidelines very differently, you’re never going to get a computer that doesn’t follow the rules exactly or allow prejudice to bias their decisions.”

Humans are inconsistent where robots are incapable of being anything but consistent

Hiring processes, when conducted by humans at least, have always been problematic because bias is so often unconscious. Personality and psychometric testing, blind auditions, webcam interviews and nameless CVs are on the rise, but in a face-to-face environment, anything from gender, race, clothing, education and accent can provide an unwitting platform for discrimination. Humans are inconsistent where robots are incapable of being anything but consistent.

The problem is that as well as putting these robots to work, humans are also the ones inputting the data enabling them to do that work. “There is no such thing as a neutral algorithm,” says Laurel Riek, associate professor of computer science and engineering at University of California, San Diego. “If the system is using some metric for decision making regarding employment, who came up with that metric, what data is it based on, and how is it being applied?”

This problem was highlighted earlier in 2016 when Microsoft was forced to take its AI chatbot Tay off Twitter just hours after its launch. Robots learn from the humans they are programmed by, followed by those they interact with; it wasn’t long before Tay assimilated the conversations and opinions of those around her and posted a series of racist, sexist tweets and denied the holocaust.

“Bias can creep in very easily with learning systems, and depends entirely on the data they’ve been trained on,” says Prof Noel Sharkey at Sheffield University. This means if an organisation is already an old boys’ network of employees from similar socio-economic and educational backgrounds, a robot instilled with the existing blueprint of that workforce cannot hope to make much of a diversifying impact. So despite all good intentions, it seems unlikely that robots like Matlda will become commonplace in the interview room. We may be more biased, but until a robot is able to adjust its thinking regardless of the programming it’s received – itself a contradiction in terms – the most important kind of interaction will have to remain human.

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