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UI design

As explained in Delivering Machine Learning Projects: From design to deployment

UI design is the design of a software application's user interface and its interactive presentation.

UI design shapes how a software system presents and supports interaction. Begin by understanding users, usability constraints, and the consequences of misunderstanding the system before building automated behavior. section 9.4.3

For ML-enabled systems, turn interface concerns into explicit design checks:

  • State clearly what the system can and cannot do, and how well it works. section 9.4.3
  • Show information relevant to the user's current context and account for its social and cultural setting. section 9.4.3
  • Avoid unfair social, gender, or racial bias in information and instructions. section 9.4.3
  • Make failure recoverable by supporting correction and allowing users to turn the system off. section 9.4.3

Interface failure as operational risk

A hospital booking system ignored users who were older, unfamiliar with computers, ill, or disoriented. The Boeing 737 MAX example shows how concealed automated behavior and missing activation indicators can leave users unable to understand or respond to system actions. section 9.4.3

Table 9.6 AI design guidelines (adapted from Amershi [3]) (view table figure)

When to use

Number

Design guideline

Initially

G1*

Make clear what the system can and can’t do.

G2*

As well as describing the system’s capabilities, clarify how well the system works.

During interaction

G3

Time proactive interactions with regard to the user’s current task and environment.

G4*

Show information relevant to the user’s current context.

G5*

Provide information in a way that the user can reflect on its social and cultural context.

G6

Make sure that information and instructions don’t reflect unfair social, gender, or racial biases.

When wrong

G7

Support efficient invocation to make it easy for the system work.

G8*

Make it easy to turn the system off, even after failing.

G9

Support efficient correction by making it easy to correct and edit the systems output and actions.

G10*

Scope services when in doubt. If the system isn’t sure, then get more information from the user or gracefully degrade the service when unclear about the users’ goals.

G11

Give users access to explanations about the system’s behavior.

Over time

G12

Remember recent interactions and maintain those in memory to make it easy for the user to use that memory to get service.

G13

Learn from user behavior; personalize the system to the user.

G14*

Adapt cautiously. Limit disruptive and unexpected changes to the system’s behavior, especially where the user has needs based on their expectations.

G15*

Encourage granular feedback. Enable users to provide meaningful information on performance and ensure that there is a mechanism to do that.

G16

Let users know when their input changes the behavior of the system.

G17*

Allow the users to monitor and control how the system behaves.

G18*

Notify users about changes. Inform the users when the AI’s capabilities are updated or enhanced so that they can modify their expectations.

Use the AI design guidelines as a checklist across initial disclosure, interaction, and failure handling. section 9.4.3

Treat UI design as a production deliverable alongside application design, logging and monitoring, implementation, and testing; record its scope in the documentation plan. section 4.3.5

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes
Defines usability risks and practical AI interface guidelines, but stops short of implementation-specific UI techniques.
Examples
Uses a hospital booking system, Boeing 737 MAX MCAS, and an AI design-guideline checklist.
What it assumes
Assumes a system and its users are identifiable, while teaching practitioners to surface usability constraints before automation.
What you can do after
Evaluate/understand interface risks and apply explicit checks for capability, context, bias, correction, and shutdown.
How it connects
Connects usability, design thinking, machine learning, bias, explainability, and failure recovery.

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This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.