All in the mix: AI is about augmentation, not just automation

AI is real to many of us in business. Yet much of the debate about machine learning, AI and the use of Big Data remains hyperbolic. Headlines screeching about robot takeovers and mass job losses might do well for click-rates. Data specialists and early adopters may wax lyrical about the technological advances being made, and how these processes far outstrip the capacity, speed and computational power of mere mortals. But both polarities are far behind the more complex, interesting reality – and they’re missing out on the real news for us humans.

The real headline, for all the clicks it won’t attract, is that we needn’t worry, nor throw all our confidence behind AI: it will never completely replace humans. But that doesn’t mean it’s not going to change the way many of us work – and for the better.

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AI in surprising places: Working with humans

We often think of AI having an impact in manufacturing. Processing. Picking and packing. This has already happened, so it’s no surprise that AI finds its place in the collective imagination at the low-skilled end of the employment market.

But we’re behind the curve if we only think of its application in simple, or easily automated, tasks. Because some of the best areas for AI to work are the least obvious, and the most complex.

Let’s take a challenging example: HR functions. Frequently underestimated, and with fewer data-driven tools to support and empower them, AI, machine learning and computer science appear at first to be an awkward fit with the most people-focused of functions.

On one hand, we may feel that much of HR can be automated straight off the blocks. HR advisors? There’s an app for that. Who needs a human to be involved if HR is processes and number-crunching? We should embrace efficiency and get machines to do it.

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On the other, we might be tempted to shield HR from AI entirely. How can machines possibly understand the complexities of human emotions? It’s essential to have human contact at every stage of the employee lifecycle. Machines can’t support, challenge, encourage, negotiate, empathise or engage.

But neither of these are correct. They both assume that AI is a sledgehammer; an unstoppable, inhuman force. It isn’t: it’s capable of nuance, fine balance and delicacy when blended with human insight.

That’s not to say that it can’t be used as such. Amazon’s difficulties with its CV scanning AI are a testament to the ease of its misapplication. But the problem wasn’t with the technology – nor the algorithms. The problem was that we asked a machine to do a human job.

CVs are about judgement, picking up on cultural signals and reading between the lines as much as they are about the facts (even if we concede that they are facts, and not fiction) that they impart. If you feed a particular data set to a machine and ask it to analyse it within a particular set of parameters, it will give you that particular kind of answer. The skew lies in the human input, and is presented back to us – and sometimes amplified – by the algorithms we determined. Take Norman, MIT’s ‘psychopathic’ AI, which took all its data from the dark web, or Tay, Microsoft’s foul-mouthed chat bot.

CVs, after all, are not the best predictor of how well a person will perform in role. We know that data analytics and cognition are far better indicators of someone’s ability to do the role we need them to do. Perhaps we should take a step back from CVs anyway, rather than applying new technology to a fundamentally flawed system of recruitment?

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The perfect blend?

Machines will do as they’re told. And in that lies both the difficulty and the beauty of using them in HR.

It’s a reason to limit HR’s reliance on AI alone. At Cognisess we blend AI, machine learning and computer science using the latest psychological and cognitive insights. We also like to think that AI needs a psychologist. That’s not just because we’re psychologists; nor are we comforting ourselves that we’ll be useful entities in AI’s Brave New World. It’s because we understand that AI is, essentially, psychotic. Humans need to calibrate, supervise and manage it, and always will.

It’s also the most important reason that we should – and will – use AI much more in HR in future. Because we are all human, we all have conscious and unconscious biases. Sometimes they’re useful; sometimes they’re not. Often, we try our best to identify and minimise them. But they’re always there: they’re an intrinsic part of being human.

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Let’s look at that another way: AI doesn’t have to understand its own unconscious bias, because it has none. AI does not need diversity and inclusion training. It’s incapable of taking an instant dislike to someone, secretly wondering whether someone’s planning on starting a family, or hiring someone who’s pleasingly similar to them.

AI offers an entirely level playing field. And sometimes (though not all the time), that’s just what we need – and need to be seen applying.

The business case for predictive people analytics

AI is much better at predicting someone’s future performance than a human is. Human mechanisms rely on methods from before great scientific strides were made into human psychology and data science: CVs and face-to-face interviews; assessment centres and IQ tests. Now, we can use AI combined with machine learning and computer science to spot true potential, so businesses can spend time face-to-face just with the people they really know are capable of doing a great job. Cognisess recently saved International Hotels Group £1.25 million per year on just one round of recruitment by reframing their assessment centres.

Across the companies we work with, our predictive people analysis process has reduced the cost and time it takes them to hire by 60%, and the number of their ‘bad hires’ by 25%, leaving more money in the pot for achieving business goals. CIOs and FDs take note: AI is a powerful tool for business efficiency.

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If you can identify someone’s strengths and weaknesses at the outset, not only do you know they’re the right fit – they’re less likely to leave. The cost of recruitment, of investment trying to force square pegs into round holes through wasted training and development, and high churn rates can sink companies large and small. Once the best people are on board, AI will help identify people’s potential in order that businesses can develop this, helping get the most from employees that will be more happy and engaged.

Long term, taking an approach to recruitment, retention and development that appreciates someone’s whole offer as a candidate or employee will establish a culture of healthy, respectful employer/employee relationships.

A balancing act

AI’s ability to collect, process and analyse enormous amounts of data will lighten some of HR’s more time-consuming and routine tasks. Augmenting – rather than fully automating – HR functions will allow HR professionals to do what they have always done in a far more informed way, driven by data and science and without losing human insight.

Just as technology and data experts will need to work with a broader group of colleagues in business to apply data-driven insights, these same tech experts will become further expert at blending the skills and attributes of humans and machines. In an ideal world, we can both apply AI to the best of its abilities, and support humans to make the best, most informed decisions they can.

Dr Boris Altemeyer believes that soft skills, such as Emotional Intelligence, will always be needed for emotional connection, support and challenge. But some jobs will inevitably be affected.
Written by Dr Boris Altemeyer, psychologist and Chief Scientific Officer at Cognisess

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