NIST AI Risk Management Framework
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  • NIST AI Risk Management Framework
  • GRN 1: Risk Management Documentation
    • GRN 1.1 - AI Legal and Regulatory Requirements
    • GRN 1.2 - Trustworthy AI Characteristics
    • GRN 1.3 - Transparent Risk Management
    • GRN 1.4 - Risk Management Monitoring
  • GRN 2: AI Organisation Structure
    • GRN 2.1 - Roles and Responsibilities
    • GRN 2.2 - AI Risk Management Training
    • GRN 2.3 - Executive Responsibility
  • GRN 3: AI Internal Stakeholders
    • GRN 3.1 - AI Risk Decisions Making
  • GRN 4: Organisational Commitments
    • GRN 4.1 - AI Risk Organisational Practices
    • GRN 4.2 - AI Organisational Documentation
    • GRN 4.3 - Organisational Information Sharing Mechnism
  • GRN 5: Stakeholder Engagement
    • GRN 5.1 - External Stakeholder Policies
    • GRN 5.2 - Stakeholder Feedback Integration
  • GRN 6: Managing 3rd-Party Risk
    • GRN 6.1 - 3rd Party Risk Policies
    • GRN 6.2 - 3rd Party Contingency
  • MAP 1: AI Application Context
    • MAP 1.1 - Intended Purpose of AI Use
    • MAP 1.2 - Inter-disciplinary AI Stakeholders
    • MAP 1.3 - AI's Business Value
    • MAP 1.4 - Organisations AI Mission
    • MAP 1.5 - Organisations Risk Tolerance
    • MAP 1.6 - Stakeholder Engagements
    • MAP 1.7 - AI System Requirements
  • MAP 2: AI Application Classification
    • MAP 2.1 - AI Classification
    • MAP 2.2 - AI Usage by Humans
    • MAP 2.3 - TEVV Documentation
  • MAP 3: AI Benefits and Costs
    • MAP 3.1 - AI System Benefits
    • MAP 3.2 - AI Potential Costs
    • MAP 3.3 - AI Application Scope
  • MAP 4: 3rd-Party Risks and Benefits
    • MAP 4.1 - Mapping 3rd-Party Risk
    • MAP 4.2 - Internal Risk Controls for 3rd Party Risk
  • MAP 5: AI Impacts
    • MAP 5.1 - AI Positive or Negative Impacts
    • MAP 5.2 - Likelihood and Magnitude of Each Impact
    • MAP 5.3 - Benefits vs Impacts
  • MRE 1: Appropriate Methods and Metrics
    • MRE 1.1 - Approaches and Metrics
    • MRE 1.2 - Metrics Appropriateness and Effectiveness
    • MRE 1.3 - Stakeholder Assessment Consultation
  • MRE 2: Trustworthy Evaluation
    • MRE 2.1 - Tools for TEVV
    • MRE 2.2 - Evaluations of Human Subjects
    • MRE 2.3 - System Performance
    • MRE 2.4 - Deployment Valid and Reliable
    • MRE 2.5 - Regular Evaluation of AI Systems
    • MRE 2.6 - Evaluation of Computational Bias
    • MRE 2.7 - Evaluation of Security and Resilience
    • MRE 2.8 - Evaluation of AI Models
    • MRE 2.9 - Evaluation of AI Privacy Risks
    • MRE 2.10 - Environmental Impact
  • MRE 3: Risk Tracking Mechanism
    • MRE 3.1 - Risk Tracking and Management
    • MRE 3.2 - Risk Tracking Assessments
  • MRE 4: Measurement Feedback
    • MRE 4.1 - Measurement Approaches for Identifying Risk
    • MRE 4.2 - Measurement Approaches for Trustworthiness
    • MRE 4.3 - Measurable Performance Improvements
  • MGE 1: Managing AI Risk
    • MGE 1.1 - Development and Deployment Decision
    • MGE 1.2 - Risk Mitigation Activities
    • MGE 1.3 - Risk Management of Mapped Risks
  • MGE 2: Managing AI Benefits and Impacts
    • MGE 2.1 - Allocated Resources for Risk Management
    • MGE 2.2 - Sustained Value Mechanism
    • MGE 2.3 - AI Deactivation Mechanism
  • MGE 3: Managing 3rd-Party Risk
    • MGE 3.1 - 3rd Party Risk are Managed
  • MGE 4: Reporting Risk Management
    • MGE 4.1 - Post-Deployment Risk Management
    • MGE 4.2 - Measurable Continuous Improvements
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  1. MAP 3: AI Benefits and Costs

MAP 3.1 - AI System Benefits

NIST AI RMF (in the playbook companion) states:

MAP 3.1

Benefits of intended system functionality and performance are examined and documented.

About

AI system benefits should counterbalance the inherent risks and implicit and explicit costs. To identify system benefits, organizations should define and document system purpose and utility, along with foreseeable costs, risks, and negative impacts. Credible justification for anticipated benefits beyond the status quo should be clarified and documented.

Actions
  • Utilize participatory approaches and engage with system end users to evaluate system efficacy and interpretability of AI task output.

  • Incorporate stakeholder feedback about perceived system benefits beyond the status quo.

  • Align system requirements with intended purpose and document decisions.

  • Perform context analysis related to time frame, safety concerns, geographic area, physical environment, ecosystems, social environment, and cultural norms within the intended setting (or conditions that closely approximate the intended setting).

Transparency and Documentation

Organizations can document the following:

  • Have the benefits of the AI system been communicated to users?

  • Have the appropriate training material and disclaimers about how to adequately use the AI system been provided to users?

  • Has your organization implemented a risk management system to address risks involved in deploying the identified AI solution (e.g. personnel risk or changes to commercial objectives)?

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Last updated 2 years ago