Written by Mirantha Jayathilaka and Dr. Janak Gunatilleke

In this article, we consider the importance of user research in AI projects and describe our approach at Mindwave.

AI is the new electricity

This is a phrase often heard from the pioneers of the technology. It is meant to transform industries from healthcare to manufacturing, from logistics to retail. But there is a clear lesson that we can draw from our experience with electricity over the past years — it is an enabler. Not all products or solutions that use electricity changed the way we live today. But some of them really did.

The impact of AI is fairly evident from the success stories that have emerged so far. Take smart assistants for example, devices such as Amazon Echo and Google Home. Manually coding a software system that could interpret the many variations of human conversation effectively seemed like a far fetched goal a few years ago. But recent AI techniques in natural language processing (NLP) have enabled this capability for software and the applications have soon followed into our lives. Smart assistants today have transformed the way we search on the web, listen to music and even how we buy.

But it is important to see what drove the adoption of this new technology. Is it the intrinsic affordance of the technology alone or something else? We argue that the main reason for the huge popularity of smart assistants lies in how they changed the fundamental way we communicated with the machines. Verbal dialogue is the most natural method with which we communicate with each other, hence it becomes the best method in the case of human-machine interface as well. Thus we make a clear distinction — product success was due to solving a major challenge in human-machine interaction, and AI was just the enabler.

Problem Identification — First step in AI development workflow

Problem identification should become the initial step in any AI development workflow. We like to highlight two separate avenues where problems can occur with respect to a given end-product.

I. Problems between user and the product

Product adoption is often driven by the user experience and the many aspects that revolve around it. Hence it is an area that requires proper user research and feedback generation.

Take an example use case where the idea is to develop a virtual psychiatrist to treat patients with mental and emotional challenges. Here the objective is to provide the most suitable advice on the basis of the input from the patient regarding his/her mental and demographic status. Looking at a scenario of psychiatrist-patient interaction, the effectiveness of diagnosis and treatment depends heavily on the expressivity of the patient in describing what he/she feels. Here a challenge is identified — how does one facilitate this nuanced interaction via a software interface? — this should become an initial concern for the development workflow of this product.

II. Problems with inefficiencies and improvements in an existing product

Often opportunities for automation addressing inefficiencies and challenges with regard to innovation could surface in existing products.

Consider the use case of a movie recommendation system. One means of implementation is to carry out a traditional execution which produces a new set of recommendations given the user’s past choices. In this case, the instructions are hardcoded by the designers of the system. But a more advanced and effective strategy would be to understand how the users across the system have reacted to the recommendations in the past, learn from the historical data and improve the recommendations with respect to user clusters automatically. This is the challenge identification which leads us to the next point.

Assess if AI is the enabler for the solution

Following the identification of the problem and potential solutions, it is vital to assess the possible methods of developing a solution and work out if AI would act as the enabler or not.

Connecting with the virtual psychiatrist use case from above — a method of improving user expressivity in the virtual psychiatrist system might be to allow the user to input free text into the system, explaining his/her issue. Now in order to process and retrieve elements of the text which are important to the diagnosis, NLP techniques become a requirement. Here we choose AI to be the enabler.

Let us consider another use case of a virtual pre-diagnosis for a condition concerning physical health. In this instance a specific condition has to be determined by discrete inputs from the patient, such as age, symptoms, severity level etc. Here, a more traditional programming approach of producing the results would fit better. With respect to the accuracy and transparency of the process, software that is programmed explicitly to make decisions comes above the existing AI approaches in this case.

Final thoughts

Jeff Catlin makes a firm point in his Forbes article about identifying the need and the desired outcome before thinking of the technology aspect of an AI project. He argues that building a business case for AI isn’t so different from building one for any other business problem. Accurate study on the feasibility and return on investment will drive AI adoption and make the best use of the technology. Robert Pearl goes on to mention how AI could live up to its hype in the healthcare industry, where he emphasises how tech companies often focus squarely on the technology while routinely overlooking the human fears and frustrations that AI can cause. Addressing these needs such as ethicality and security can surface new opportunities and areas of growth for AI in the much-anticipated field of healthcare

At Mindwave, we follow the above approach towards AI developments in the field of healthcare, carefully identifying the problems through deep user research and feedback. We assess the feasibility of using AI in solving the identified problems and focus on strong evaluation strategies to measure their impact.

We argue that the use of AI for the technology’s sake, without a proper understanding of how it can solve real problems and impact our lives, can lead to failed investments of time and money. It can damage the trust towards the technology and eliminate potential long-term outcomes that can be favourable to everyone.

If you have any feedback or would like to chat about any of the above points, please drop Janak an email via [email protected]