6 min read

Machine Learning is driving many major innovations happening around the world. But while complex algorithms drive some of the most exciting inventions, it’s important to remember that these algorithms are always designed. This is why incorporating UX into machine learning engineering could offer a way to build even better machine learning systems that put users first.

Why we need UX design in machine learning

Machine learning systems can be complex. They require pre-trained data, and depend on a variety of variables to allow the algorithm to make ‘decisions’. This means transparency can be difficult – and when things go wrong, it isn’t easy to fix.

Consider the ways that machine learning systems can be biased against certain people – that’s down to problems in the training set and, subsequently, how the algorithm is learning. If machine learning engineers took a more user-centric approach to building machine learning systems – borrowing some core principles from UX design – they could begin to solve these problems and minimize the risk of algorithmic bias.

After all, every machine learning model has an end user. Whether its for recommending products, financial forecasting, or driving a car, the model is always serving a purpose for someone.

How UX designers can support machine learning engineers

By and large, machine learning today is mostly focused on the availability of data and improving model performance by increasing their learning capabilities. However, in this a user-centric approach may be compromised. A tight interplay between UX design practices and machine learning is therefore highly essential to make ML discernible to all and to achieve model interpretability.

UX Designers can contribute in a series of tasks that can improve algorithmic clarity. Most designers create a wireframe, which is a rough guide for the layout of a website or an app. The principles behind wireframing can be useful for machine learning engineers as they prototype their algorithms. It provides a space to make considerations about what’s important from a user perspective.

User testing is also useful in the context of machine learning. Just as UX designers perform user testing for applications, going through a similar process for machine learning systems makes sense. This is most clear in the way companies test driverless cars, but anywhere that machine learning systems require or necessitate human interaction should go through some period of user testing. 

UX Design approach can help in building ML algorithms according to different contexts and different audiences. For example, we take a case of an emergency room in an hospital. Often the data required for building a decision support system for Emergency patient cases is quite sparse. Machine Learning can help in mining relevant datasets and divide them into subgroup of patients. UX Design here, can play the role of designing a particular part of the Decision Support system.

UX professionals bring in a Human Centered Design to ML components. This means they also consider user perspective while integrating ML components. Machine Learning models generally tend to take the entire control from the user. For instance, in a driverless vehicle, the car determines the route, speed, and other decisions. Designers also include user controls so that they do not lose their voice in the automated system.

Machine Learning developers, at times may unintentionally introduce implicit biases in the systems, which can have serious or negative side effects. A recent example of this was Microsoft’s Tay, a Twitter bot that started tweeting racist comments spending just a few hours on Twitter. UX Designers plan for these biases on a project by project level as well as on a larger level, advocating for a broad range of voices. They also keep an eye on the social impact of the ML systems by keeping a check on the input (as was the case with Microsoft Tay). This is done to ensure that an uncontrolled input does not lead to an unintended output.

What are the benefits of bringing UX design into machine learning?

All Machine Learning systems and practitioners can benefit from incorporating UX design practice as a standard. Some benefits of this collaboration are:

  • Results generated from UX enabled ML algorithms will be transparent and easy to understand
  • It helps end-users understand the product functioning and visualize the results better
  • Better understanding of algorithm results builds user’s trust towards the system. This is important if the consequences of incorrect results are detrimental to the user.
  • It helps data scientists better analyse the results of an algorithm to subsequently make better predictions.
  • It aids in understanding the different components of model building: from designing, to development, to final deployment.
  • UX designers focus on building transparent ML systems by defining the problem through a storyboard rather than on constraints placed by data and other aspects. They become aware of and catch biases ensuring an unbiased Machine learning system.
  • All of this, ultimately results in better product development and improved user experience.

How do companies leverage UX Design with ML

Top-notch companies are looking at combining the benefits of UX design with Machine Learning to build systems which balance the back-end work (performance and usability) with the front-end (user-friendly outputs).

Take Facebook for example. Their News Feed Ranking algorithm, an amalgamation of ML and UX design, works on two goals. The first is showing the right content at the right time, which involves Machine Learning capabilities. The other is enhancing user interaction by displaying posts more prominently so as to create more customer engagement and increase user dwelling time.

Google’s UX community has combined UX Design with machine learning in an initiative known ashuman-centered machine learning (HCML). In this project, UX designers work in sync with ML developers to help them create unique Machine Learning products catering to human understanding. ML developers are in turn taught how to integrate UX into ML algorithms for better user experience.

Airbnb created an algorithm to dynamically alter and set prices for their customers units. However, on interacting with their customers, they found that users were hesitant of giving full control to the system. Hence the UX Design team altered the design, to add functionalities of minimum and maximum rent allowed. They also created a setting that allowed customers to set the general frequency of rentals. Thus, they approached the machine learning project with user experience keeping in mind.

Salesforce has a Lightning Design System which includes a centralized design systems team of researchers, accessibility specialists, lead product designers, prototypers, and UX engineers. They work towards documenting visual systems and abstraction of design patterns to assist ML developers.

Netflix has also plunged into this venture by offering their customers with personalized recommendations as well as personalized visuals. They have a personalized artwork or imagery to portray their titles. The artwork representing their titles is adjusted to capture the attention of a particular user. This, in turn, acts as a gateway into that title and gives users a visual perception as to why a TV show or a movie is good for them. Thus helping them achieve user engagement as well as user retention.

The road ahead

In future, we would see most organizations having a blend of UX Designers and data scientists in their teams to create user-friendly products. UX Designers would work closely with developers to find unique ways of incorporating design ethics and abilities in machine learning findings and predictions. This would lead to new and better job opportunities for both designers and developers with further expansion on their skill sets. In fact, it would give rise to a hybrid language, where algorithmic implementations will be consolidated with design to make ML frameworks simpler for the clients.

Content Marketing Editor at Packt Hub. I blog about new and upcoming tech trends ranging from Data science, Web development, Programming, Cloud & Networking, IoT, Security and Game development.


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