+3.8 Artificial Intelligence and Machine Learning in Identity Systems

3.8 Artificial Intelligence and Machine Learning in Identity Systems

Identity solutions use artificial intelligence (AI) and machine learning (ML) in various ways, such as improving the performance of biometric matching systems, automating evidence or attribute validation, detecting fraud, and even assisting users (e.g., chatbots). While the potential applications of AI and ML are extensive, these technologies may also introduce new risks or produce unintended negative outcomes.

The following requirements apply to all uses of AI and ML in the identity system, regardless of how they are used:

All uses of AI/ML SHALL be documented and communicated to organizations that rely on these systems. The use of integrated technologies that leverage AI/ML by CSPs, IdPs, or verifiers SHALL be disclosed to all RPs that make access decisions based on information from these systems.
All organizations that use AI/ML SHALL provide information to any entities that use their technology on the methods and techniques used for training their models, a description of the data sets used in training, information on the frequency of model updates, and the results of all testing completed on their algorithms.
All organizations that use AI/ML systems or rely on services that use these systems SHOULD implement the NIST AI Risk Management Framework ([NISTAIRMF]) to evaluate the risks that may be introduced by such systems.
All organizations that use AI/ML systems or rely on services that use these systems SHALL perform and document privacy risk assessments for personal information and data processed by such systems.

Further information on practice statements and their contents can be found in Sec. 3.1 of SP 800-63A. ↩

For more information about privacy risk assessments, refer to the NIST Privacy Framework: A Tool for Improving Privacy through Enterprise Risk Management at https://nvlpubs.nist.gov/nistpubs/CSWP/NIST.CSWP.01162020.pdf. ↩

Redress generally refers to a remedy that is made after harm occurs. ↩

1. Pārskats

1.1 Atsauces

1.2 Identificētās prasības

1.3 Saistītie tiesību akti

2. Identificētās prasības

Prasības

3. Saistītie tiesību akti

Tiesību akti
  • NIST -

    EN: Publications of the National Institute of Standards and Technology (NIST), U.S. Department of Commerce, https://www.nist.gov/, from their electronic versions (OSCAL; SP 800-63-4 from its HTML version, https://pages.nist.gov/800-63-4/). Works of NIST employees are not subject to copyright protection in the United States (17 U.S.C. § 105); the OSCAL content is dedicated to the public domain worldwide (CC0 1.0); outside the United States NIST grants the right to reprint its technical series publications in all formats (NIST copyright statement). Republished courtesy of the National Institute of Standards and Technology; each document names its citation. Divided into sections and converted into the GRCA format, the wording unchanged. Only the versions published by NIST are authoritative.

    DE: Veröffentlichungen des National Institute of Standards and Technology (NIST), U.S. Department of Commerce, https://www.nist.gov/, aus ihren elektronischen Fassungen (OSCAL; SP 800-63-4 aus ihrer HTML-Fassung, https://pages.nist.gov/800-63-4/). Werke von NIST-Beschäftigten sind in den USA urheberrechtlich nicht geschützt (17 U.S.C. § 105); die OSCAL-Inhalte sind weltweit gemeinfrei gestellt (CC0 1.0); außerhalb der USA räumt das NIST das Recht ein, seine Technical-Series-Publikationen in allen Formaten neu zu veröffentlichen (Copyright-Hinweis des NIST). „Republished courtesy of the National Institute of Standards and Technology“; jedes Dokument nennt seine Zitierangabe. In Abschnitte aufgeteilt und in das GRCA-Format umgewandelt, der Wortlaut unverändert. Maßgeblich sind allein die vom NIST veröffentlichten Fassungen.