What Makes AI UX Different from Normal UX

What Makes AI UX Different from Normal UX

Welcome to the wild world of AI UX, where the rules of traditional UX design often take a backseat to a new set of challenges and opportunities. In this lesson, we're going to dive into why designing UX for AI is like trying to train a cat: unpredictable, sometimes rewarding, and occasionally leaves you wondering what just happened.

The Unpredictable Nature of AI

AI UX is as unpredictable as a toddler in a candy store. Traditional UX design is like setting up a well-organized library: you know what goes where. AI UX? It's more like setting up a playroom and hoping the kids don't decide to paint the walls.

Why This Matters for PMs

For PMs, understanding the unpredictable nature of AI is crucial. It means planning for scenarios where the AI doesn't behave as expected and ensuring there's a fallback plan. Your users need to trust that even if the AI goes off-script, they're not left hanging.

Real World Example: Google Photos

Remember that time Google Photos thought your dog was your Aunt Susan? Yeah, that's unpredictability in AI for you. Google Photos uses machine learning to categorize images, but sometimes it misfires. The key here is how Google handles this unpredictability—by giving users easy ways to correct mistakes, making the AI feel less like a rogue artist and more like a helpful assistant.

Feedback Loops and Transparency

Feedback loops in AI are like telling your GPS it took you through a swamp instead of the highway. The AI learns from its mistakes, improving over time. But it needs your input to do so.

Why This Matters for PMs

As a PM, it's your job to ensure that users have clear, easy ways to provide feedback. This not only helps the AI improve but also builds trust with your users as they see their input leading to tangible improvements.

Real World Example: Spotify

Spotify uses feedback loops to tailor its music recommendations. When you skip a song, you're silently telling Spotify, "Not my jam!" Over time, Spotify learns your preferences. The transparency here comes from how Spotify explains its recommendations, making users feel like they're in control of their music destiny.

Transparency: The Key to Trust

Transparency in AI UX is like a good magic trick—impressive, but you want to know how it's done. Users need to understand why AI makes the decisions it does, otherwise it feels like a black box.

Why This Matters for PMs

Your users aren't just passive recipients; they want to be co-pilots. By explaining how AI comes to its decisions, you invite them into the process, fostering trust and engagement.

Real World Example: Spotify's "Made for You"

Spotify goes a step further with its "Made for You" playlists, providing users with insights into why certain songs are included. This transparency not only educates users but also builds trust, as they feel the AI is working with them, not just for them.

Diagrams

Feedback Loop in AI

graph TD;
    A[User Input] --> B{AI Processes};
    B --> C[AI Output];
    C --> D{User Feedback};
    D --> B{AI Processes};

Caption: This diagram shows the feedback loop process where user input and feedback are constantly cycling to improve AI output.

Exercises

Exercise 1: Identifying Unpredictability in Your Product

Instructions

1. Take a look at the AI features in your product.
2. Identify parts of the user experience where unpredictability could occur.
3. Develop two strategies for how to handle these unpredictable moments.
4. Present your findings to your team.

Expected Outcome

You should have a clear understanding of potential unpredictability in your product and have actionable strategies to mitigate its effects.

Hints

• Consider user feedback channels.
• Think about error messages and how they're communicated.

Difficulty: Medium

Exercise 2: Designing a Transparent UX

Instructions

1. Choose an AI feature in your product.
2. Draft a user interface that explains how the AI comes to its decisions.
3. Test this design with a small group of users and gather feedback.
4. Refine your design based on user feedback.

Expected Outcome

A user interface that clearly communicates AI decision-making processes, enhancing user trust.

Hints

• Use simple language.
• Visual aids can be helpful.

Difficulty: Medium

Examples

Example: Google Photos and User Correction

Scenario

Google Photos often miscategorizes images, but it allows users to correct these mistakes easily.

Explanation

This example illustrates the importance of designing AI systems that gracefully handle errors. By allowing user corrections, Google Photos improves its AI while maintaining user trust.

Example: Spotify's User-Centric Recommendations

Scenario

Spotify uses user feedback to refine music recommendations, displaying transparency in how playlists are curated.

Explanation

Spotify's approach to transparency and feedback loops shows how clear communication with users can enhance trust and user engagement.