Will the real expert please stand up?

Applying the term expertise through a traditional lens, can be challenging in itself, let alone if we consider the term in a world where AI is ubiquitous. Given the pace of technological advancements, it might be timely to interrogate the relevance of human-centred notions of expertise and explore constructs of expertise that may more appropriately align with the changing technological landscape of our society.

If I asked you to name an expert in any field of your choosing, would you be able to?

Most of us would probably know of at least one expert, whether they come from the sporting arena, the academic setting, the arts domain or science field, there are certain individuals who are well recognised and stand out from the crowd due to their skill set, knowledge and contribution to society.

As such, expertise is something most of us have an appreciation for.

We understand that a person who we consider to be an expert, has the knowledge base and skill set they can utilise and apply to different scenarios within their field, in order to solve problems, make improvements or help others.

But expertise does mean different things to different people and there are a number of different ways to characterise expertise. Sternberg & Ben-Zeev (2001) propose that expertise is about both knowledge acquisition and the application of knowledge to different scenarios. Additionally, they suggest experts have developed, and apply, analytical thinking skills, creative thinking skills, and practical thinking skills to problems. Most experts are not stumped by the prospect of change, but rather they adapt to changing situations and can often anticipate future events. Importantly, they also engage in self-reflection and have the ability to monitor and accurately assess their own performance, using their learnings as a basis for self-improvement (Ericsson, 1996; Glaser, 1996).

So in a nutshell we could say an expert is someone who:

·        is receptive to change

·        has a strong knowledge base (about their field/subject matter)

·        has a refined range of adaptable analytical, creative, practical, transferrable skills they can tap into and apply to help them work through challenges

·        reflects on their performance and takes their learnings to help them improve their performance, and the overall outcomes and outputs.

And whilst there is a degree of subjectivity associated with identifying expertise, it is common for experts to be acknowledged as such by the peers in their field.

However, maintaining ‘expertise’ status is increasingly difficult, due to the accessibility of information available on any given topic, the speed in which new research findings are published, the extent to which research is disseminated, and the many opportunities and channels for challenging ideas, positions and research.

But how do individuals get to a point where they are recognised as experts?

Dreyfus and Dreyfus (1980) suggest that expertise is acquired by moving through various stages of developmental progression, namely novice, competent, proficient, expert, and master. Now, whilst I do not intend to dismiss or criticise this model, there are some considerations that are worth acknowledging. For example, the original model assumed that the aim for all learners would be to progress to master stage, but this may not necessarily be the case, not everyone wants to be a master at everything they try. Sometimes a learner just wants to get to a point where they have acquired a satisfactory level of competency. Further, the model is primarily about skill acquisition, and reflects quite a linear process of developmental progression. So whilst this model is certainly applicable in many contexts, it is possible that other factors can impact on the development of expertise in any given domain, such as the quality of the content and coaching/teaching individuals are exposed to, and engage with, or the intentions and aspirations of the individual, or access to resources, limitations and opportunities in a setting.

But let’s step back for a moment and take a big picture approach to expertise.

What strikes me first and foremost is that, to date, expertise has been considered and applied in a very human-centred way. I can’t help but wonder if the theories of expertise we are applying in our current times will still be relevant and applicable in the future, given the likely advances in technology and artificial intelligence?

What will expertise even look like in the future?

How will we define expertise as we increasingly engage and apply Artificial Intelligence and Machine Learning to help complete tasks and perform roles across a wide range of fields, including the health and education sector?

Will humans be able to claim a level of expertise or will expertise only be reserved for the machines that will process and analyse information, more efficiently than humans and likely beyond human capabilities, as they mine extensive databases to ascertain what we need, what we want, and when we need or want it?

But let’s consider the idea of expertise in the future by thinking about some (hopefully) relatable scenarios:

In about 20 years from now, when faced with a health issue, will we request expertise from a doctor to help diagnose an ailment, or will we seek a diagnosis from a machine, one that makes a decision by efficiently analysing masses of data about symptoms similar to those we present with, and one that further can suggest a diagnosis with limited, if any, human bias?

Or in another scenario, when faced with two options to get home from a night out, who do you organise to get you home safely? A driver from a rideshare service who has an unblemished record and over 20 years of experience? Or a driverless vehicle? Which has the greatest expertise to get you home safely? Can we, or should we, even apply expertise to machines? And who or what would you trust more?

I recently heard that one of the key things that differentiates humans and machines, is a human’s ability to make judgements as opposed to calculations performed by machines. It was argued that judgements require perception, which was suggested is currently beyond the scope of AI. So if we are talking about trusting machine expertise then perhaps the questions relate more to the quality and trustworthiness of the data and the algorithms applied.

But how we frame expertise in the future will be interesting. Expertise is what we look for in an individual or group when looking for answers to a problem, or when looking to better understand a phenomenon. We look to someone who knows more than we do, so that we can learn from them, so they can solve a problem, maximise outcomes, outputs and results, or advance the thinking and practice in a particular domain. So in the era of AI who or what will we deem to have the greatest expertise when it comes to decision-making or solutions for a particular problem we are faced with?

I think the notion of expertise has never been challenged more than is currently the case. I am not referring to challenging the credentials of someone who is perceived to be an expert in a particular field, but rather in moving forward, I am suggesting a broader approach to how we construct a definition of expertise.  A conceptualisation of ‘expertise’ which is validated and ethical, and which capitalises on human and machine expertise, to align with our evolving world.