UX Audits for AI Apps | Challenges & Best Practices

Name any app with complex, intractable problems and tons of data, and you’ll find an app powered by artificial intelligence (AI). There are many direct and indirect ways AI is being leveraged in the apps we use every day — from virtual assistants like Alexa and Siri to the algorithms powering Instagram and TikTok timelines and the recommendations that shape our media consumption on Netflix and Spotify.

AI is more accessible than ever before, not just to the big players in the industry but also to many startups that are experimenting with AI apps. But, “success” for AI apps is not just about embedding AI into apps — it is also about delivering solid user experiences. Unless AI-powered apps can consistently deliver great experiences, they won’t succeed in capturing users’ minds and hearts. Hence, these apps need UX audits.

UX audits are systematic, data-based evaluations of an app’s user interface (UI) and user experience (UX). They help app makers identify design flaws and usability roadblocks that hinder user satisfaction. Implementing recommendations from a UX audit can help app makers resolve the usability issues plaguing their products and align their designs with user needs and expectations.

Challenges in UX Audits for AI Apps & Best Practices

While UX audits promise a long list of benefits for AI-powered apps that need higher user adoption and engagement rates — it is not that easy to audit these apps:

Complexity of AI Systems

AI systems are inherently complex due to the sophisticated algorithms and machine learning models that underpin their functionality. The intricacies of these systems can obscure how users engage with the app. This complexity poses significant challenges for UX auditors.

For example, the AI algorithms used in apps like Facebook continuously learn from user interactions to optimize content delivery in real time. The dynamic nature of the app indicates that the quality of user experience can shift dramatically, hour to hour, based on algorithm updates or changes in user behavior. It shows a pressing need for UX auditors to possess a foundational knowledge of AI technologies.

Best Practices

Auditors must work closely with the app’s data scientists and engineers to demystify its unique AI processes before they give suggestions on how to improve the app’s overall usability.

Lack of Transparency in AI-Powered Apps

There is a lack of transparency that is inherent in most AI apps. AI apps often operate without providing users any insight into how decisions are made or how data is processed. The opacity can lead to user frustration, distrust, and anxiety. It is also a challenge while performing UX audits as auditors may not understand how certain recommendations are being made by the AI.

Best Practices

To mitigate these challenges, auditors should:

• Ask for transparency from the app makers regarding their app’s AI algorithms
• Work with the app makers to implement feedback loops where users can report their experiences and seek clarifications on AI decisions
• Work with the app makers to develop educational resources that guide users through the functionalities of the AI system

Measurement of Success in UX Audits for AI-Powered Apps

Defining effective metrics for success during UX audits of AI-powered apps is very difficult due to the unique nature of these systems. Traditional app audit metrics are:

• Conversion rates
• Accessibility
• Error rate
• Task time
• Broken links
• Task completion rates
• Time on task

Most of them do not capture the intricacies of user interactions on AI apps. For example, evaluating an AI-driven customer service chatbot solely based on response time does not reflect the quality of the interactions or actual user satisfaction rates. Tracking how many users click on AI-powered recommendations does not give a complete picture of ‘success’. An effective UX audit would also analyze whether users actually found the chatbot responses helpful or enjoyed the AI-recommended content and continued watching it.

Best Practices

To accurately assess user experience in AI apps, UX auditors should consider these practices:

• Involve end-users in defining what success looks like for them
• Implement a continuous feedback loop where metrics are regularly reviewed and refined based on user input
• Compare your metrics against industry benchmarks to understand where your AI app stands relative to competitors
• Develop a comprehensive set of metrics that combine both qualitative and quantitative data
• Quantitative metrics should focus on specific interactions that are critical to the app’s success
• Qualitative metrics should include user feedback gathered through interviews/surveys, which explore users’ thoughts on the AI app’s effectiveness, responsiveness, and overall satisfaction

Error Handling in UX Audits

While auditing AI apps, distinguishing between “errors” and “failures” is vital for understanding user expectations and gauging system reliability. It is also a big challenge for auditors.

Errors are often perceived as minor inconveniences, while failures can lead to significant consequences, such as safety issues or loss of user trust. For example, an AI music recommendation system that occasionally suggests irrelevant songs may be seen as making errors but a self-driving car that fails to stop at a red light is a catastrophic failure. This distinction is vital for prioritizing error-handling strategies based on their severity during UX audits.

This is not the only error-handling challenge UX auditors and designers have to deal with while working with AI apps. In many cases, users may not fully grasp why errors occur in AI systems, leading to frustration and diminished trust. This misunderstanding often arises from insufficient feedback or unclear communication regarding the nature of the errors.

Best Practices

Here are some best practices Ux auditors need to follow to mitigate the issues related to error handling:

• Clearly define and categorize errors and failures within the system
• Implement a structured framework that classifies issues based on user perception and the context of use. The classification helps teams prioritize which errors to address urgently and which can be tolerated.
• Optimize error messaging to enhance user understanding.
• Make sure the app’s UI design provides clear, actionable feedback when an error occurs, explaining what went wrong and how users can rectify it.
• If a user inputs incorrect data into an AI form, the system should indicate that an error occurred and guide them on how to correct their input effectively. Checking for these error messages should be a central part of the UX audit.

The context in which an AI application operates significantly influences how users perceive errors. High-stakes situations (e.g., on medical AI apps) necessitate more stringent error handling than low-stakes environments (e.g., entertainment AI apps).

UX auditors should tailor error-handling mechanisms according to the stakes involved in user interactions. For high-stakes apps, they must implement robust validation checks and prompt users to review critical inputs before proceeding. In lower-stakes scenarios, lighter feedback that encourages exploration without heavy penalties for mistakes will suffice.

Feedback Loops in AI-Powered Apps

Another challenge UX auditors face when evaluating AI-powered applications is the establishment and effectiveness of feedback loops. AI systems are inherently probabilistic and can make mistakes, which can lead to user frustration and diminished trust if these systems do not learn from their errors. Users may encounter unexpected outcomes, such as an AI misidentifying an object or providing wrong recommendations, creating a gap between user expectations and actual performance.

Best Practices

The following best practices can mitigate feedback loop challenges:

Guide App Makers in Implementing Robust Feedback Mechanisms

UX auditors should ensure that AI systems have comprehensive feedback loops that allow for continuous learning and improvement:

• This involves integrating explicit feedback mechanisms — thumbs-up/thumbs-down ratings, and comment sections.
• It also involves integrating implicit feedback derived from user behavior analytics like interaction logs and usage patterns.

For example, if a recommendation engine suggests content that users frequently skip, UX auditors should analyze this implicit feedback. They should then ask the app makers to recalibrate the recommendation algorithm accordingly.

Educate Users on AI Learning Processes

It is crucial for UX auditors to advocate for clear communication about how the AI system learns and improves over time. This involves recommending onboarding tutorials that explain the training process and the probabilistic nature of AI outcomes. By detailing how user interactions help refine AI recommendations, users can better understand why certain suggestions may not always align with their preferences initially.

Use Dual Feedback Approaches

To effectively harness both explicit and implicit feedback, UX auditors should promote a dual feedback strategy that combines insights from both types. For instance, if users frequently play a particular song but do not explicitly rate it positively, auditors should recommend analyzing this behavior alongside explicit ratings received. The comprehensive view allows for better tuning of the AI model by recognizing patterns that may not be immediately apparent through one type of feedback alone.

Establish Continuous Monitoring Systems

UX auditors should advocate for ongoing monitoring systems that track user interactions and collect data on performance metrics after deployment. Using customer service reports, app store reviews, and in-product surveys helps gather insights on how users engage with the AI app over time.

Respond to Feedback Effectively

It is essential for UX auditors to ensure that there are mechanisms in place for responding to user feedback promptly and transparently. This could involve acknowledging received feedback, explaining how it will be used to improve the system, and communicating updates or changes made based on user input. By demonstrating responsiveness to feedback, UX auditors can enhance user trust and engagement with the AI app.

Conclusion

UX auditors play a crucial role in the success of AI-powered apps. Their guidance can make these apps more user-centered, trustworthy, and capable of adapting to user needs.

To maximize these benefits, AI app makers should seek out world-class UX audit service in the early stages of the product development process. This proactive approach will help ensure that user needs are prioritized from the outset.

App makers should also host training sessions for their teams and their UX auditors to educate them about the AI technologies fueling the app and how they should align with the principles of user-centered design.