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The HAX Playbook is an interactive tool for generating interaction scenarios to test when designing user-facing AI systems.

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Introduction

The HAX Playbook is an interactive tool for generating interaction scenarios to test when designing user-facing AI systems.

A default survey about designing an AI system is included in this repository. It provides a demonstration of the features supported by the web app.

The web app's features include fully customizable contextual help cards, salutation message, farewell message, and a list of tasks based on survey results. All of these components are read from a JSON file. See the Extension section for more details.

Getting started

To run the app, open a terminal in the root directory of the repository and run:

$> npm install
$> npm start

Extending survey functionality

The survey and textual content are defined in JSON files. To add your own content, modify the JSON files as instructed below.

Survey

The survey is defined in src/data/survey.json. SurveyJS is the library used to render the survey. To create your own survey, use the survey creator on the SurveyJS website.

To export the survey you've created, click on the JSON Editor tab and copy the contents, then paste into src/data/survey.json.

Survey guidelines

To make sure your survey is supported, please adhere to the following guidelines:

  • Only use the radio group, dropdown, and boolean survey components.
  • Specify a name for each question. A default name like question1 will be provided automatically in the SurveyJS builder. It's better to use a descriptive name to make the generated content.json file more readable.

How to create a cascading survey

The survey included with the HAX Playbook has a cascading-question effect, causing new questions to appear when the user selects an answer. Cascading questions are implemented by configuring the visibility rules for questions in the survey. The visibility rules can be configured in the SurveyJS builder or in the JSON file. To configure visibility in the SurveyJS builder, select the question to modify, then select the Visible if rule in the Logic section of the Properties toolbar. A cascading-question effect is implemented by using the rule {previous_question_name} is not empty. Effectively, this rule says, "if an answer has been selected for the previous question, then show this question."

Textual content

All of the content outside of the survey itself is defined in src/data/content.json. This content is used to enrich the user experience by providing an introduction, instructions for each survey question, contextual help for each choice, and tasks for each choice. HTML is supported inside each component.

Generating a starter file

A starter content.json file can be generated from a survey.json file. The starter file will contain a skeleton of the data with empty strings ready for you to fill in. There are two options to generate it.

Script

To use the script, open a terminal at the repository's root directory and do:

$> node scripts/generate.js <path_to_survey.json> <output_file_name>

API

If you can't run node locally, there is also an API available. Send your survey.json in the request body.

POST https://surveymeta.azurewebsites.net/api/generate

Explanation of properties in content.json

Top-level properties

  • introduction: The message displayed before the survey begins. This is optional.
  • surveyInstructions: The message to display above the survey. This property has a title and a message.
  • taskInstructions: The message to display above the list of tasks. This property also has a title and a message.

Question properties

  • name: The name of the question. Must match the question name in survey.json.
  • category: The category for the question. Tasks will be consolidated into sections by category.

Choice properties

  • name: The name of the choice. Must match the choice name in survey.json.
  • definition: A message that will appear below the survey choice, along with the ? button.
  • examples: Contains an array of examples. These examples are shown in the help pop-up when the ? button is clicked for that choice.
  • taskCard: Defines the tasks corresponding to the question. Tasks are shown in the survey results page.

Example properties

  • name: The title of the example. Appears in the help dialog as a heading.
  • details: The message for the example. HTML is supported.
  • visibleIf: A logic expression that controls whether or not this example is visible. This can be used to provide contextual examples based on the user's choices in the survey. Syntax documentation

Task card properties

  • message: A message to show for the entire group of tasks.
  • tasks: An array of tasks.

Task properties

  • name: The title of the task.
  • details: The message for to the task. HTML is supported.
  • visibleIf: A logic expression that controls whether or not this task is visible. This can be used to provide contextual tasks based on the user's choices in the survey. Syntax documentation

Syntax for visibleIf logic

SurveyJS includes a robust domain-specific language for controlling the flow of the survey. See https://surveyjs.io/Documentation/Library#conditions for the official documentation and examples. Configuring logic in the survey creator is a good option if you don't want to handwrite code.

Here are some basic examples to get you started.

  • Check if the user selected a certain choice: "visibleIf": "{questionName} = 'choice'"
  • Check if the user did not select a choice: "visibleIf": "{questionName} <> 'choice'"

Contributing

Check CONTRIBUTING page.

Research and acknowledgements

This project materializes and implements ideas from ongoing research on Human-AI interaction. Here is a list of development and research contributors:

Current Contributors: Nicholas King, Jingya Chen, Kathleen Walker, Mihaela Vorvoreanu, Xavier Fernandes, Juan Lema, Adam Fourney, Saleema Amershi

Research Contributors: Matthew K. Hong, Adam Fourney, Derek DeBellis, Saleema Amershi

ACM Reference Format: Matthew K. Hong, Adam Fourney, Derek DeBellis, and Saleema Amershi. 2021. Planning for Natural Language Failures with the AI Playbook. In CHI Conference on Human Factors in Computing Systems (CHI ’21), May 8–13, 2021, Yokohama, Japan. ACM, New York, NY, USA, 16 pages. https://doi.org/10.1145/3411764.3445735

Microsoft Open Source Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

License

This project is licensed under the terms of the MIT license. See LICENSE.txt for additional details.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.