Five things to help you achieve AI-driven UX

It is hard to separate Machine learning and UX design as they are both invaluable to improve the quality of a product experience. You may have heard about how UX designers will play a vital role in the evolution of AI. Machine learning is built upon complex algorithms that learn from different data sets or human interactions. Over a period of time, it gains insights to act on a certain instruction or predict outcomes on its own. However, machine learning often produces the kind system which is hard to understand, mostly when the data sets are complicated or an unknown algorithm is used.

This is where design becomes relevant to make the products more accessible to the user. From the perspective of a designer, it is their ability to keep the user in mind and deliver positive interactions that matters the most. With more AI in UX design, it means that designers will have to know more about data and how an algorithm responds to a specific data set. For example, if you are designing a fitness app, you need to study the health care data to make more plausible assumptions. The data can say a lot about where the user belongs to, age groups, locations, body types etc.

Without the proper knowledge of how the product built using machine learning affects the user, it is difficult to make a designing decision that benefits the user in any way. The future of machine learning will depend a lot on how designers apply their own knowledge and the effectiveness of AI to bring a better user experience for every individual.

Here are some design principles you need to know before designing good AI products:

Making machine learning transparent

AI derives meaningful insights from complex data sets to produce recommended actions. But, there are chances that the AI generated content may not be accurate. This is seen mostly in cases when there is not enough data or feedback to learn from.

We need to differentiate or mark out the content generated by an algorithm so people know that the data is not fully accurate. This way a customer will know whether to trust the data or not.

Explain how machines think

Sometimes, it becomes incredibly hard to explain how a machine learning algorithms has come up with a certain recommendation. But, we should let people know the reason why they are seeing such results. Of course, we don’t need to give a detail account about how a neural pathway works.

A better understanding of how a ML system work is essential to keep people interested in the system. We should give users hints about what the algorithm does or what data it uses.

Building the right communication

We should understand how to make our design systems more practical and widely usable for all types of users. Sometimes, it’s funny how things turn up when people try to communicate with chat bots. When encountered with unexpected questions like how would you describe ‘bot’ to your grandma? The answer is ‘my grandma is dead’. Extensive testing can help us prevent these issues. However, a designer needs to be fully aware of user expectations and communicate these issues to the developer so they can fine-tune the algorithms to prevent bad responses.

Providing the right training data

A UX team provides valuable guidance to the engineering team based on the insights collected from training data. While the engineers are focused on finding the best algorithm for the task, UX people help define user expectations to ensure the end product addresses needs more effectively. Google reportedly hires “content specialists”, experts in the domain of the product who help build training data set. In an AI project, you need designers and developers to work closely with each other to design better products and experiences.

User testing for AI products

Depending on the application and what data is available, there are different types of testing methods for AI products. However, Testing the UX of AI products is considered more difficult than for regular apps. You can Test the AI UX with methods like the Wizard of Oz testing. The Woz method is commonly used in the initial prototyping stage to check for any inaccuracies in product response.

Building an AI-enabled product has many benefits-it is able to predict user behavior and immediately understands the best options to perform the function. However, with the increasing adoption and acceptance of new AI-appliances, it is important to set the right expectations so people will know what they can or can’t achieve with the AI product. You should allow users to provide feedback at all times, and even come up with suggestions on how to improve the product. Understanding the dynamics of how AI UX products perform in real-life situations and mitigating the UX challenges is required to create the best experience possible.