The "Machine-Learning-Warning-Systems" project offers insights and a practical framework for developing machine learning systems that provide warnings instead of making decisions. This approach promotes human involvement and oversight, ensuring your systems remain helpful while handling uncertainty.
Key Topics Covered:
- Accountability
- AI Ethics
- Auditability
- Calibration
- Counterfactuals
- Decision Systems
- Explainable AI
- Fairness
- Governance
- Human-in-the-Loop
- Interpretability
- Machine Learning
- MLOps
- Product Design
- Responsible AI
- Risk Management
- Systems Thinking
- Uncertainty
- User Experience (UX)
- Warnings
These steps will help you get started with our application.
Before downloading, ensure your system meets the following requirements:
- Operating System: Windows 10 or later, macOS Mojave or later, or a recent version of a Linux distribution
- Memory: At least 4 GB of RAM
- Disk Space: Minimum 200 MB of free space
To run the application, follow these steps:
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Visit the Releases Page: Go to the Releases page to find the latest version.
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Download the File: Click on the link for the latest release version suitable for your operating system to download the installation file.
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Install the Application:
- For Windows: Double-click the downloaded
.exefile to start the installation wizard. Follow the prompts to complete the installation. - For macOS: Open the
.dmgfile and drag the application to your Applications folder. - For Linux: Unpack the tar file to a location of your choice and follow the included instructions to install.
- For Windows: Double-click the downloaded
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Run the Application: Locate the installed application on your device and open it.
This tool helps users create systems that warn rather than decide. Here are some main features you can expect:
- Regimes vs. Decimals: Understand how to select the right output for your machine learning models.
- Levers Over Labels: Learn about the importance of inputs in the decision-making process.
- Reversible Alerts: Create alerts that can provide feedback for user decisions.
- Anti-Coercion UI Patterns: Design interfaces that respect user choice and agency.
- Auditable Processes: Ensure your model decisions are transparent and can be reviewed.
- The "Warning Card" Template: A practical template to implement warnings in your designs effectively.
For a detailed explanation of each feature and to follow along with the framework, please refer to our Documentation.
If you encounter issues, please check our FAQ section in the documentation. For further assistance, feel free to reach out via the Issues page on GitHub.
Join the discussion around machine learning ethics and accountability. Engage with others who are also interested in creating responsible AI systems.
We welcome contributions from users who wish to help improve this project. To contribute, please check our Contributing Guidelines in the repository.
Thank you for using "Machine-Learning-Warning-Systems"! We hope it helps you design effective machine learning systems that prioritize human oversight.