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 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
