Month: October 2017

Short talk on Future of Work and Skills

Last week I gave a short talk on the future of work and skills at the 10th anniversary of People Per Hour online freelancing platform in London. Here is the text of the talk.

Good evening and happy birthday, People per Hour! Thank you very much for the opportunity to join the celebration!

I was asked to share brief reflections on the future of work and learning. So I’d like to offer a few general observations about the changing nature of work, then outline a few specific skills and attributes that I think will be crucial moving forward.

Changing nature of work

I want to begin by suggesting that much of the current discourse on the future of work –that intelligent machines will destroy jobs causing mass unemployment and societal upheaval –  seems ahistorical. Historically, introduction of new technologies has always destroyed some jobs and created others. There doesn’t seem to be a compelling reason to expect that the current cycle of technology development will be radically different in terms of this fundamental pattern.

Secondly, just because jobs and work tasks can be automated doesn’t mean that they will be. Technology is not exogenous to our society. It is part of an overall socio-technical system of the society. Historically, the deployment of technology has been enabled as well as constrained by broader forces – economic, legal, ethical, societal and political considerations. We don’t know just exactly how these broader factors will play out and how they may play out differently in different countries.

Thirdly, scholars studying the impact of artificial intelligence have pointed out that currently few if any intelligent machines and systems are truly autonomous, but that rather the role of humans is often obscured in these systems.  Madelene Elish, who is a cultural anthropologist at Columbia University, argued that “when we examine autonomous systems we must look not to the erasures of the human, but to the ways in which we, as humans, are newly implicated”…

Information scientists Bonnie Nardi and Hamid Ekbia coined the term ‘heteromation’ to denote the paradigm of work premised on the division of labour between machines and humans. They argue that unlike the technologies of automation, the aim of which is “to disallow human intervention at nearly all points in the system”, the technologies of heteromation “push critical tasks to end users as indispensable mediators, drawing the humans back into the computational fold”. This sorts of heteromative systems are currently in evidence, from Amazon Mechanical Turk to factory floors in Germany and China where humans increasingly collaborate with robots in our production systems. It seems likely that at least in the medium-term, it is such heteromative arrangements rather than the pure automation paradigm that will predominate.

The historian Yuval Harari has pointed out that “not just the idea of ‘a job for life’, but even the idea of a ‘profession for life’ will soon come to seem antediluvian”. Harari’s argument suggests that we will have to be flexible, not just in terms of our knowledge and skills, but also sector and the location where we work, our professional identity and be prepared to drastically reorient ourselves several times within our careers.

This takes me to the future of learning and skills…

Future of learning and skills

I’d like to suggest to you that, as we undergo a range of these horizontal and vertical reorientations implied in Harari’s argument, the ability to actively initiate rather than only endure these transitions will become a crucial mindset.  Organisational scholars Lynda Gratton and Andrew Scott pointed out that people who are successful in undergoing transitions share at least three important abilities. First, they are self-reliant and possess the necessary self-knowledge to understand who we they are and what they may be in future. Second, they have dynamic and heterogeneous networks to provide them with role models of what they could be and how to become it. And third, they are risk takers, they have tolerance for uncertainty, and openness to experience “to act their way into change”.

I’d argue that presently our public discourse and policy landscape is overly focused on the formal, institutionalised forms of education and training. Limited recognition and value is given to self-directed and self-initiated forms of learning that people undertake throughout their lives, individually or in cooperation with others. The problem is that the educational institutions we have come to rely on for our learning and training are very conservative and slow. And it is questionable that they will be able to provide the rapid and dynamic reskilling and reorientation that individuals will require.

So I’d like to suggest that moving forward the most important attribute that individuals must acquire is the ability to self-regulate and self-direct their learning. This means, being strategic and dynamic in identifying our learning goals and strategies, being proactive in seeking feedback on our learning and work, continuously studying the market to understand and identify the changing skill requirements, strengthening our personal self-efficacy, being self-reflective and able to dynamically change our learning strategies when these are not working. These attributes will be increasingly required of everyone, not just those in managerial or highly-skilled jobs. These individual attributes have always been important psychologically, but I’d argue that they have now become crucially important economically as well.

My last point is that moving forward some radically new skills will also be required. One of these is for us to learn to cooperate effectively with the intelligent machines in our increasingly heteromative workplaces. The emergent forms of algorithmic management of labour necessitate the ability to work productively in a supervisory relationship with a machine. A key question I would like to ask is this: in a workplace where your boss is a machine, where algorithms allocate and dynamically price work, algorithms record and evaluate the quality of the output, algorithms assess workers’ performance and recommend learning activities to improve performance what are the skills, attributes and dispositions needed to work productively with these machines and how can people develop these capabilities?  Presently, our policy landscape appears to overlook these fundamentally new skill requirements altogether and our educational and vocational systems are doing little to prepare people for this new reality.

Let me conclude by observing that to succeed in the future of work, we have a greater need for personal autonomy, self-reliance and self-direction. At least for some time to come there will be no accepted code of rules to absolve us of the challenge of individual decision making in our work and in our learning. Thank you.