{
    "identifier": "ark:\/88434\/mds2-3398",
    "accessLevel": "public",
    "contactPoint": {
        "hasEmail": "mailto:[email protected]",
        "fn": "Harold Booth III"
    },
    "programCode": [
        "006:045"
    ],
    "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-3398",
    "title": "Dioptra Test Platform",
    "description": "Source code, documentation, and examples of use of the source code for the Dioptra Test Platform.Dioptra is a software test platform for assessing the trustworthy characteristics of artificial intelligence (AI). Trustworthy AI is: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair - with harmful bias managed1. Dioptra supports the Measure function of the NIST AI Risk Management Framework by providing functionality to assess, analyze, and track identified AI risks.Dioptra provides a REST API, which can be controlled via an intuitive web interface, a Python client, or any REST client library of the user's choice for designing, managing, executing, and tracking experiments. Details are available in the project documentation available at https:\/\/pages.nist.gov\/dioptra\/.Use CasesWe envision the following primary use cases for Dioptra:- Model Testing:  -- 1st party - Assess AI models throughout the development lifecycle  -- 2nd party - Assess AI models during acquisition or in an evaluation lab environment  -- 3rd party - Assess AI models during auditing or compliance activities- Research: Aid trustworthy AI researchers in tracking experiments- Evaluations and Challenges: Provide a common platform and resources for participants- Red-Teaming: Expose models and resources to a red team in a controlled environmentKey PropertiesDioptra strives for the following key properties:- Reproducible: Dioptra automatically creates snapshots of resources so experiments can be reproduced and validated- Traceable: The full history of experiments and their inputs are tracked- Extensible: Support for expanding functionality and importing existing Python packages via a plugin system- Interoperable: A type system promotes interoperability between plugins- Modular: New experiments can be composed from modular components in a simple yaml file- Secure: Dioptra provides user authentication with access controls coming soon- Interactive: Users can interact with Dioptra via an intuitive web interface- Shareable and Reusable: Dioptra can be deployed in a multi-tenant environment so users can share and reuse components",
    "language": [
        "en"
    ],
    "distribution": [
        {
            "accessURL": "https:\/\/github.com\/usnistgov\/dioptra",
            "format": "Github Repository",
            "description": "The USNIST Github location where source code is located.",
            "title": "Dioptra Github Repository"
        }
    ],
    "bureauCode": [
        "006:55"
    ],
    "modified": "2024-07-24 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "theme": [
        "Information Technology:Cybersecurity"
    ],
    "keyword": [
        "AI; Trustworthy AI; Test; Evaluation; Adversarial Machine Learning; Machine Learning; TEVV"
    ]
}