Fault-Tolerant AI UX Design

When building products powered by machine learning or generative AI, the user interface must be a direct reflection of the algorithm's performance. Most product failures in AI occur when the UI assumes 100% accuracy (the "Silver Button" trap) while the model only delivers 20% accuracy. This framework helps you design interfaces that turn algorithmic errors into acceptable user experiences.

Core Principles

1. Map UI Density to Hit Rate

The number of options presented to a user should be the inverse of your model's hit rate.

• Low Hit Rate (e.g., 20%): If your model is only right 1 out of 5 times, you must show at least 5 items simultaneously. This ensures the user likely sees one relevant "hit" without feeling the product is broken.
• High Hit Rate (e.g., 90%+): You can afford a "Silver Button" or "Single Play" UI where the algorithm makes a definitive choice for the user.

2. Balance Recall vs. Discovery

Identify if the user is in a "Recall" mode or a "Discovery" mode to determine UI density and media richness.

• Recall (90% of most sessions): Users want to get back to a known session, playlist, or document. Use high-density UIs (lists, small icons) to allow for quick scanning.
• Discovery (10% of sessions): Users want to "break their taste bubble." Use low-density, high-pixel UIs (large cards, auto-playing video/audio) because the user needs more context to evaluate a new, unknown suggestion.