Artificial Intelligence: I think, therefore AI – and I feel Human – Part II

In a part 1 exploration of the shock-horrors and adventure lands of artificial intelligence, I looked at a theory that machine learning and plasticity of technologies, as yet in their infancy but at a pace in their development, will before too long prove capable not simply of performing functions that human beings have done, but of performing those functions in the way that we would do it and without the limit of accepting conclusions that the human brain has reached.
Yet you may join me in hoping that the future doesn’t bring such a clear outcome. So, here is a list to take comfort in and why I think that there is a bit bots at the top of the class against the brownie points and black marks of human brain-power.

If thinking is about removing any trace of doubt, providing scientific and empirical analysis and conclusion in a perfectly mechanistic way, then machine learning surely cannot be beaten. We beat it only by agreeing to do so and conceding.
A first stage of next-generation AI leaves people and machines as happy companions, in all but the most routine of jobs. We can safely assume that some of the “big data” the tech will miss. In HR, today’s tech gives us some good clues as to what may come next for AI. Look at what it currently does well and not so. Making short shrift of volume calculation, searching for needles in haystacks (job applicants?!), synching and linking systems to source constantly updated data is already delivering us great advancement in people management.
But in each case the apparent result or the “insightful” MI must be subject to a rigorous filter as to appropriateness, meaning and value. For example, predictive analytics (read here a top 10 FAQs on HR Analytics) are still a long way from factoring into every performance case the complete circumstances that we as people understand to be relevant. Or in scanning the online activity of potential hires, machine learning might more readily get to flightiness, job dissatisfaction and industry experience than to assessing values or competency fit. Tech is not so great at contextualising, finding exception, bending rules in mitigation or strategising.
But in a second stage AI journey ahead, where the data becomes more and more complete, it is harder to be completely certain that our thinking as people allows us to stay ahead. Imagine that those machine tools really did come to learn about all of the data out there and analytical reasoning which was logical. At work, where do humans then out-perform?
Remember from part 1 that when Descartes talked about thought, he included consciousness, awareness and subjectivity. He believed that the duality of mind and body meant that we had a mechanistic, animal functioning – like a machine – which was aside from our soul. In today’s context of emerging AI, I wonder if this is a useful notion. Soul is not a word we use much in today’s psychology but we get what it means.

Perfect results at work involve feelings, judgments, intuition. These are beyond logical capture. We noted above how people are best at spotting exception and context – and assessing value and meaning. Potentially even this type of awareness becomes replaceable. Soul does not. The best that machine learning could presumably do is to copy what souls seemed to do before or to look for random soul patterns.

I do not suggest that HR is the only profession with relevance in an age of true artificial intelligence. In a new age of reason, a new age of HR could be one example of 6 uniquely human concepts at work and at play with AI:

1. Artistry. Humans understand our expressive enjoyment of what we see, do and use. Consider whether we find things beautiful, elegant, quirky, fun, imaginative or new. Find relevance for reward and motivation, employee engagement, retention, employer branding and learning styles.
2. Self-actualisation. A classic and a key concept in acknowledging a striving for empowerment, influence, control. Take away this potential and would we sabotage our machinery before it advanced? HR need to read the talent need to strive and find identity at work.
3. Merit in imperfection. Think about how a less than perfect result can sometimes be just the right one when it comes to organisational tactics. Again, HR can find relevance in promoting teamwork and optimum performance, navigating politics, relieving stress.
4. Companionship. I deliberately avoid the word “social” because I don’t mean social contact through media or technology platforms. I mean physical and visual awareness of one another to take cues of empathy and respect. Apply this concept to talent management, wellbeing, partnerships. There is also a place for tea and sympathy.
5. Deceit. As long as there is a machine-human interface then there is this potential. We could fool tools and we will need an arbiter. Biometrics will have limits. We are brilliant at work-arounds. Perhaps the new specialisms will focus on management technique or business psychology.
6. Trump cards. There is often an overwhelming factor that trumps all. I see this a lot with HR systems, where that factor could be the need for change or for no change. Likewise think about the maverick CEO or rogue player. Sometimes the right answer is right because “it just is”. And here, humans will hold the trump card for sure.

Notice that the suggestions here do not apply only at strategic level but at the transactional too. In the context of HR, both leadership and operational specialisms can survive supporting advanced machine learning – perhaps in people management skills, care, psychology and talent – and, yes, analysts and HR systems pro’s too.

I think and I feel, therefore I am comfy in cheering on HR technology and AI in the workplace. We can forge ahead in our profession and relax in appreciating that our amateur-ness is quite possibly the HR expert, people team and organisational ace. We can forge ahead knowing that a key part of our professionalism is in, quite simply, being human.

Photo Credit:  studiostoks / 123RF Stock Photo

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