A strong user experience (UX) has consistently been central to the success of any product development lifecycle, especially AI products that require careful designing processes. Understanding how users will interact with AI-powered products – their requirements, challenges, and experiences – has always been fundamental to effective product design. This article will outline the 7 fundamental UX principles you can use in designing an AI-driven document parsing products.
UX Principle 1: Visually differentiate AI-generated results
AI tools can process large amounts of data and provide aggregate summary information that proves extremely useful. However, the algorithms are imperfect. Designing products that indicate whether users are accessing AI-generated data or human-generated data, empowering them to make informed decisions about its reliability.
In Rossum's case, the AI algorithms analyze documents and extract relevant information like invoice numbers, dates, amounts, and line items. However, to maintain transparency and empower users to make informed decisions about the reliability of the extracted data, Rossum provides a confidence score for each data point extracted.
For example, when Rossum processes an invoice, it may provide a confidence score alongside each extracted field indicating the system's level of certainty in the accuracy of that data. If a particular field has a low confidence score, it signals to the user that they should review and verify that information manually.
Moreover, Rossum allows users to easily validate and correct any inaccuracies in the extracted data. If a user disagrees with a data extraction or believes that a field needs correction, they can easily make adjustments within the platform. This correction process not only improves the accuracy of future extractions but also enhances user trust and confidence in the system.
This principle is crucial for building user trust and understanding. Users need to know when they're interacting with AI-generated content versus human-generated content. Providing transparency enhances user confidence in the system. [1]
UX Principle 2: Involve users in AI learning
At first glance, AI may seem like magic. Even experts can find it difficult to explain how AI algorithms work and what results they produce. However, knowing how the system works and what data it uses is useful for users.
For example, when the answer is incorrect, users can modify the result. The product should keep both wrong and corrected data for further improvements. It means that whenever a user reviews the output, it becomes smarter and can analyze similar documents more accurately and efficiently in the future. Users should understand that their interactions contribute to the system's learning and improvement over time.
UX principle 3: User control
Finding a balance between AI automation and user control is critical. Users should feel in control of the interaction, and AI should act as an aid, not a replacement. Offer users settings or preferences to adapt AI behavior, and provide clear options for users to override AI decisions.
The key is to balance automation and user control. Giving users the ability to override AI decisions enhances a sense of empowerment and ensures that AI acts as a useful tool rather than a black box.
By offering both automation and user control, for example, Adobe Acrobat's PDF data extraction feature strikes a balance that empowers users to efficiently extract data while still maintaining control over the process. This promotes a sense of user empowerment and ensures that AI technology acts as a helpful tool rather than a black box.
Another example, is when KorScript’s answer is incorrect, users can modify the result. KorScript keeps both wrong and corrected data for further improvements. It means that whenever a user does a review of KorScript output, it becomes smarter and in the future analyze similar documents more accurately and efficiently. Users should understand that their interactions contribute to the system's learning and improvement over time.
UX principle 4: Education and Onboarding
Informing users about how to engage with AI-powered functionalities and managing their expectations regarding the AI's capabilities are essential for fostering a positive user experience. For instance, a clear onboarding tutorial can help users understand how to interact with AI-driven software. Use tutorials, tooltips, and help centers to educate users about AI features and best practices.
Educational features should include interactive tutorials and informative tooltips strategically integrated throughout the platform. These features serve as indispensable guides for users, offering insights into the functionalities of our AI driven system. By providing users with clear instructions and explanations, we enhance user proficiency, ensuring a smooth and frictionless experience from the moment of initial interaction.[2]
UX principle 5: Error Handling
Because AI can make mistakes, effective error handling in AI design involves creating intuitive ways for users to know what has happened. Our product provides clear feedback and navigation options to address errors, ensuring users are guided through the resolution process seamlessly. This minimizes user frustration and increases overall satisfaction.
IBM Watson Document Processing detects the error during the processing pipeline and sends an email notification to the user, alerting them to the issue. The email includes a summary of the processing failure, details about the corrupted file attachment, and instructions for resolving the issue (e.g., re-upload the affected invoice without the corrupted attachment). Additionally, IBM Watson Document Processing provides a dashboard interface where users can track the status of their document processing tasks and view detailed error logs for troubleshooting purposes [3].
Effective error handling is critical for maintaining user trust when AI makes mistakes. Providing clear feedback and guidance helps users understand and address errors effectively.
UX principle 6: Feedback Loop
Provide clear feedback, suggest easily accessible corrective actions, and use user data to improve AI accuracy over time. Allowing users to provide feedback on decisions made by AI can increase trust. Enabling users to provide feedback on AI decisions promotes trust and continuous improvement. Users feel empowered when they can contribute to refining the AI system based on their experiences [4].
The only thing we can do is create a feedback loop that allows a user to write a comment or suggest improvements to the AI output. This can be achieved by simply adding feedback functions, and clearly communicating what the AI can and cannot do.
For example, if a user asks Siri a question and the response provided is not helpful or accurate, the user can provide feedback by saying "Hey Siri, that's not what I meant" or by tapping the feedback button in the Siri interface. This feedback is used by Apple to improve Siri's understanding and accuracy over time.
Encouraging user feedback on AI decisions fosters trust and continuously enhances system accuracy. By incorporating user input, performance is iteratively improved, aligning closely with user needs and expectations [5].
UX Principle 7: Trust and Transparency
Trust and transparency are crucial in AI-driven design. It means the product should get the job done, and be reliable, and the results should meet the users' needs. The main risk is that some users might trust the system, while others might overestimate its capabilities, which can ultimately lead to mistrust when the system does not meet their high expectations. By explaining how AI systems function and the rationale behind their choices, designers can build trust and reduce skepticism [6].
This approach includes providing clear, accessible explanations of AI algorithms, data usage, and the steps involved in generating outcomes. By focusing on these principles, designers can create AI-driven products that users trust and rely on, thus fostering a positive relationship between users and technology.
Conclusion
In the field of AI-driven product design, user experience principles play a key role in shaping successful interactions. With our product, we've tried to support the 7 fundamental UX principles to ensure that users not only benefit from the system's capabilities but also feel empowered and informed during their interactions. By visually differentiating AI-generated results, engaging users in the learning process, and giving them complete control over the system, we strive to build trust and confidence. Training and onboarding efforts further enhance user understanding and adoption, while robust error-handling mechanisms ensure smooth navigation even in the face of AI imperfections. Moreover, feedback loops serve as a means of continuous improvement, using user knowledge to improve AI accuracy over time. By transparently communicating system capabilities and decisions, we strive to create a foundation of trust that underpins every user interaction.
Recommendations:
- Continuously collect user feedback to improve the AI system and improve the user experience.
- Invest in comprehensive onboarding materials to educate users about AI features and best practices.
- Prioritize clear and intuitive error-handling mechanisms to reduce user frustration and maintain trust.
- Foster a culture of transparency by being open about AI capabilities and decision-making processes.
- Regularly evaluate and update UX design to align with changing user needs and technological advances. By adhering to these recommendations and principles, AI driven products like ours can not only streamline workflows but also cultivate a positive user experience that drives long-term success and user satisfaction.
References:
1. Smith, J., & Johnson, A. (2023). Enhancing User Trust in AI Systems: Strategies for Visual Differentiation of AI-Generated Results. Journal of User Experience Design, 8(2), 45-58.
2. Brown, C., & Wilson, D. (2022). Onboarding Users to AI-Driven Platforms: Best Practices and Strategies. International Conference on Human-Computer Interaction, 345-359.
3. https://www.ibm.com/blog/ibm-watson-discovery-resolving-error/
4. Norman, Don. The Design of Everyday Things: Revised and Expanded Edition. Basic Books, 2013.
5. Nielsen, Jakob. "Usability Engineering." Morgan Kaufmann, 1994.
6. https://www.nist.gov/news-events/news/2021/05/nist-proposes-method-evaluating-user-trust-artificial-intelligence-systems
7. Jones, S., Smith, J., & Patel, R. (2018). User-Centered Design Principles for
Error Handling in AI Interfaces. Journal of Artificial Intelligence Research, 47,
789-802.
About the author
I'm Meri Shahzadeyan, currently working as a Product Design Team Leader at ARQA, where I bring extensive expertise in UI/UX design to the forefront. With a rich background spanning diverse industries, I've honed my skills through hands-on experience and continuous learning. After establishing a solid foundation in graphic design, my journey into UX/UI design began to unfold. It was during this transition that I discovered a deep passion for creating user-centric experiences. This enthusiasm propelled me through various roles, including co-founding a startup, where I refined my skills in crafting intuitive interfaces that effectively marry user needs with business objectives. Additionally, I have embraced my role as a UX/UI design tutor, committed to nurturing creativity and guiding the next generation of designers.
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