Design challengesUX of AI

Every design material comes with unique opportunities and challenges. In the same way that designing an event poster is different from designing a mobile app, designing AI-driven applications is different from designing mobile apps.

Now that AI systems are beginning to show up in our day-to-day products, so do its challenges. Below, we introduce you to 9 challenges in designing user experiences of adaptive, intelligent, and semi-autonomous systems.

User Trust & Transparency

1. Explainability

Making sense of the machine and communicating to the user why the system acts the way it does

2. Managing expectations

Helping the user understand what the system can and can not do (over time) by being transparent about abilities and limitations and building helpful mental models of it

3. Graceful failure & accountability

Assume failure and design graceful recoveries. Take accountability for mistakes and minimize cost of falses for your user

User Autonomy & Controls

4. Machine teaching & user feedback

Allowing the user to teach the machine with implicit and explicit feedback loops and collecting direct data input

5. User controls & customization

Giving users the controls to customize the system/algorithm to their needs and intervene with the course of a model if needed

6. Data privacy & security

Collect, handle, and store user data with care. Be transparent about who can access what data and why while acknowledging their ownership

Value Alignment

7. Computational virtue
Translating subjective human needs, values, and experiences into algorithmic parameters the model can optimize for

8. Bias & inclusivity
Mitigating harmful bias and guarding inclusivity in data and models to ensure fair treatment for all

9. Ethics & (un)intended consequences
Unprecedented scale, speed and complexity call for a new level of thoughtfulness and responsibility in anticipating impact and (un)intended consequences

10. […………..]
You tell me

These challenges are discussed in more detail in the e-book user experience design for machine learning I wrote and published in collaboration with Adyen and Awwwards and available for download here.

We're looking to build up a library of design patterns to build empowering and ethical user experiences of AI-driven interactions. The patterns will be generated from research and crowd-sourced by and for designers like yourself.

If you've worked with one of the design challenges, or have ideas for design patterns, please share your experiences to contribute to continuous research.

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If you're passionate about shaping the future of AI x design, consider contributing or getting involved.

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