47% of jobs may taken by robots

Professor Michael Obsborne published a paper 2 years ago which suggested 47% of jobs are at high risk of being taken over by robots. 47% is a staggeringly high number but what jobs are safe from machines? And how will the take over happen? Graihagh Jackson put these questions to Michael...

Mike - As much as 47% of current US employment might be at high risk of automation over the next 20 years.

Graihagh - This was published a couple of years ago and there's a link to it on our website - naked scientists dot com and you know what? Michael is so well placed to talk about this, as he's not only an associate professor of machine learning at the University of Oxford but a co-director of the Oxford mountain programme of technology and employment... In other words he knows a lot about intelligent about machines and how they impact society.. 

Mike - So I have these two strands - these two hats that I wear.  One is an academic researcher in machine learning itself, developing novel intelligent algorithms capable of processing data in an intelligent, sophisticated way.   But the other hat, the second title that I mentioned before is investigating the societal impact of the development of such algorithms. So our second programme looks at the employment impact that intelligent algorithms are likely to have over the next 20 years.

Graihagh - And as you heard before, 47 % of US jobs are at HIGH risk of being taken over by robots. 47%...

Mike - It's a large figure - I should put some caveats on it, so that figure does not take into account the fact that firstly, what is actually automated would depend on regulations, societal acceptance, a whole host of other factors.  It would depend on relative costs of the machines and the humans involved in those tasks and crucially we're also not taking into account in that figure the emergence of new jobs.

Graihagh - Okay then.  So how did you work this number out then - this 47%?

Mike - We're drawing on data from the US from an agency called O-NET which provides for 700 different occupations, through to those occupations, a fairly long list of quantitative measures of skill requirement.  So these measures would be things like a number between 0 and 100 saying just how much persuasion this required, or finger dexterity, or originality...

Graihagh - How do you determine that sort of thing?

Mike - It's an excellent question and you'd have to ask O-NET, but drawing upon that data we thought that those skill requirements might be an interesting way of determining what distinguishes an automatable job from a non-automatable job. So we cross-linked the skill requirements against the jobs that we had seen automated and against another list of jobs that we were fairly confident were not going to be automated within our horizon of 20 years. Using those two different categories and relating it to those different skill requirements, we actually taught a machine learning algorithm exactly the same kind of algorithm we're expecting might have impact on employment, the difference between automatable or non-automatable jobs and, on that basis, it was able to come back and tell us just how much of it might be susceptible to automation.

Graihagh - What makes a job susceptible to automation?  There you talked about un-automatable jobs and automatable jobs.

Mike - So that was another conclusion from our study, the characteristics of jobs that are not automatable and we lumped these together into what we call bottlenecks to automation.  The first of those bottlenecks was originality.  Simply put, the more original your job is the less susceptible it is to automation.

Graihagh - So painters are safe?

Mike - Painters are relatively safe, yes.

Graihagh - What other skill sets did you decide were also un-automatable?

Mike - The second of our bottlenecks was social intelligence.  So here we're thinking of skills like negotiation or persuasion.  The kinds of high level social functions that come relatively natural to us but are relatively difficult to make explicit in such a way that those tasks could be reproduced by code.

Graihagh - And the third bottleneck...?

Mike - The third bottleneck was that of autonomous perception manipulation and this one's a little bit more subtle perhaps.  It certainly is possible to get a robot to interact with physical objects in the world around it but it's important to distinguish that kind of manipulation from the manipulation that we perform in our day to day lives.  So again, to give a concrete example, I'm sitting here in the studio picking up a glass of water and in doing so, I was required to distinguish the glass from the table that sits beneath it.   Despite the fact the glass is transparent, I needed to, before I even picked up the glass, have some idea of how much it weighed so I could grip it with sufficient force but also have some expectation for its material characteristics so that I didn't shatter it when I picked it up by applying too much force.  So you can see that, even in that relative intuitive action, I had to apply a whole host of subtle tacit knowledge about my environment that again, is very difficult to reproduce in an algorithm.

Graihagh - What would that be in terms of jobs though, because I'm not lucky enough to have someone pick up my glass of water every day and feed it to me, so where would I see that day to day?

Mike - So the kinds of jobs that might be non-automatable as a result would include hairdressing, for example, it might include gardening.

Graihagh - The bottlenecks you've been describing to me in some ways are things that are very natural to us.  They don't seem to have a defined set of rules that at least I could very easily distinguish and is that, in part, why they're very hard to code for and create an algorithm for?

Mike - Exactly.  We, as humans, draw upon these deep reservoirs of tacit knowledge about our society, our companions, our environment; all stuff that's very difficult to unpick and, as I say, write out explicitly in code.

Graihagh - We've talked about the jobs that are going to be relatively safe.  Who is then at risk because, surely, a little bit of these skills are involved in everyone's jobs?

Mike - One particular category of jobs that's likely at high risk are those jobs that rely almost entirely upon storing, accessing and perhaps doing some simple processing of data.  So I'm thinking here of examples including paralegals, whose job in large part is digging through case files.  I'm thinking about people like auditors who might be required to inspect large amounts of financial data in a company.  These are the kinds of tasks, to be honest, right now might be better performed by an algorithm which is able to scale to processing much larger volumes of data, is perhaps more vigilant, they're going to be just as attentive to the millionth bit of data as the first.  They're not going to, for example, be influenced by how long it's been since they had lunch.

Graihagh - I suppose they don't need a sick day or anything like that?

Mike - Exactly.

Graihagh - How about radio producers and presenters?

Mike - Right.  So, let me say that in this kind of social interaction that we're having, for example, there's quite likely a large amount of social intelligence that's deployed that's going to be difficult to reproduce in an algorithm so...

Graihagh - Phew.  I'm safe!

Mike - Congratulations.

Graihagh - And if you want to see how safe you are, NPR have designed a nifty web tool based on Michael's work that means you can select your profession and see if you're safe or not so safe. It's on our website - naked scientists dot com or just search for will your job be done by a machine.

Mike - Firstly, a lot is going to depend on how cheap the automated resolution is relative to the human labour.  Now the scary this is, of course, software has next to zero marginal cost of reproduction so once someone has developed and intelligent algorithm capable of doing a task that was previously performed by humans that software, at least in principle, can be deployed across the entire world at next to zero cost.  So, in many cases, we might see relatively rapid transition to automated software solutions once that technology is developed.

Graihagh - Are you surprised or, indeed, do you think things should become increasingly automated?

Mike - On this shrewd question, of course, we shouldn't lose sight of the fact that these automated solutions are delivering products to us at increasingly low cost.  On the other side of things, things are not so rosy; we shouldn't lose sight of the fact that while these jobs are replaced by an algorithm, they might not be jobs that any human fundamentally wants to do.  There's an argument to be made that if a job is able to replaced by an algorithm, it's kind of below the dignity of a human being and...

Graihagh - Wow, that's pretty...

Mike - It's a strong claim I have to agree.

Graihagh - Yes...

Mike - To me, those bottlenecks that I identified before; the creativity and social intelligence are really the hallmarks of what we as human beings find satisfying and pleasurable.  Consider the tasks we do in our spare time (our hobbies), they are almost universally things that involve some degree of interacting with other humans or being creative.  So, to me, the kinds of jobs that will remain after automation are going to be increasingly satisfying and enjoyable