AI-driven technologies are all the rage. They have a significant impact on day-to-day tasks and allow us to do things we couldn't even imagine. However, we won't fully tap into their power until non-technical users can take advantage of the many capabilities that are now available at our fingertips.
Adopting a human-centered approach to designing human-AI interfaces will unlock any application's ability to achieve a broad commercial appeal. This article will discuss six critical user experience (UX) design considerations when creating AI-driven applications.

6 Key Considerations For Designing Human-AI Interactions

When you create a human-AI interface, aim for a UX tailored to your target audience's perspective and needs, while giving AI the opportunity to access the data it needs to learn about the users' behaviors and preferences. This tailored experience will enable the delivery of increasingly relevant and meaningful experience. Here's what you need to address during the design process:

Data Acquisition

AI needs training data for learning before it can perform as intended. If you don't have an available data source, then you need to create a data flow within the application to collect user information. Meanwhile, provide features independent of the AI component so data can be collected when users perform other tasks.

As such, you need to design a UX that enhances data collection. Format questions so they're clear, simple, and easy to answer. Use hints and brief instructions to guide users and only ask for the information you need when you need it to reduce friction. Also, put as few restrictions on how the answers are formatted to minimize error messages.

Visual Design and Layout

In some cases, you may need to differentiate AI-driven functions from other features or let users decide how they want to use algorithm-generated information. You can do so using different visual design treatment and/or placement to set AI content apart.

For example, placing AI-generated content in a sidebar, a separate tab, or a different section on a dashboard distinguishes this content from human-generated content. For text-heavy applications, you may present AI-generated suggestions in context with a unique visual treatment.

User On-Boarding

While many AI features are relatively intuitive and don't need much explanation, a straightforward on-boarding process can increase adoption. The on-boarding flow should explain how the system works, how data is collected, and its benefits.

Getting permission to collect data and setting expectations during on-boarding can help you build trust with the users. You should explain how the system will evolve as it interacts with users over time -- e.g., the type of information it needs to perform and the benefits of providing the necessary data to facilitate personalization.

Communication and Visualization

You can foster relationships with users by defining a communication style and personality for the AI-driven conversational interface. Suppose you don't have a conversational interface. In that case, you should still establish a tone and voice for the AI-generated content -- e.g., should it be the same style as the rest of the application, or should it have a distinct personality?

If your AI technology is used for predictive analytics, you need to develop a visualization technique to display the results. For instance, by showing AI-derived predictions differently to distinguish them from historical data, using different colors for categorization, or indicating prediction confidence with a sliding bar.

Explanation

For any predictive analytics to inspire action and create meaningful results, the users need to be confident about the predictions. This confidence can increase with explanations of the data collection process. Be sure to include a description of the results to demonstrate how the program reached the conclusions.

For instance, you can explain the reason behind a specific outcome, how AI understands the input or intent, and how users can use the information to improve subsequent outcomes. You can also use examples to illustrate the "thought process" if the logic is hard to describe.

Feedback and Learning

Input and feedback from individual users can help AI learn about their behaviors and preferences to deliver a relevant experience. You can encourage users to provide feedback by explaining how doing so can lead to better UX or improved results. Also, design a flow that gives users opportunities to provide both explicit and implicit feedback.

Explicit feedback includes voting on the usefulness of a piece of information, asking for a reason if a user overrides a strong suggestion, and allowing users to report errors. With proper instrumentation, implicit feedback collected from interactions such as clicks, views, and time is useful.

User-Friendly Human-AI Interactions

A human-centric UX is a key to the successful adoption of AI-driven technologies on a commercial scale. By collecting relevant data, encouraging adoption, meeting user expectations, and building trust, we can create human-machine partnerships that solve complex problems while increasing the effectiveness and efficiency of various systems and applications.