NIST AI-RMF Vs NIST ARIA
NIST AI RMF (Artificial Intelligence Risk Management Framework)
• Nature and Purpose: The NIST AI RMF is a framework designed to help organizations manage the potential negative impacts of AI systems while maximizing their positive benefits. It aims to foster the development and use of trustworthy AI that aligns with human rights and democratic values. It is described as a "living document" that will be regularly updated.
• Characteristics of Trustworthy AI: The framework articulates seven core characteristics of trustworthy AI systems: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair (with harmful bias managed). These characteristics are central to its guidance for responsible AI practices.
• Core Functions: The AI RMF is structured around four high-level functions that organize AI risk management activities:
→ GOVERN: Focuses on cultivating an organizational culture of risk management, establishing policies, and defining accountability structures.
→ MAP: Establishes the context of an AI system to frame its risks, including understanding intended purposes, potential impacts, and categorizing the system.
→ MEASURE: Involves employing quantitative, qualitative, or mixed-method tools to analyze, assess, benchmark, and monitor AI risks and related impacts, including rigorous software testing and performance assessment.
→ MANAGE: Deals with allocating resources to address identified and measured risks, prioritizing risk responses, and planning for incident recovery and communication.
• Scope and Application: It is a broad, comprehensive framework applicable across the entire AI lifecycle, from design and development to deployment and evaluation. It is voluntary, outcome-focused, and non-prescriptive, meaning it provides guidance rather than strict rules. It is designed to be law- and regulation-agnostic, supporting organizations operating under various legal regimes
NIST ARIA (Assessing Risks and Impacts of AI) Program
• Nature and Purpose: ARIA is a NIST evaluation-driven research program specifically designed to develop measurement methods for assessing AI's risks and impacts in real-world contexts. Its primary goal is to gather empirical evidence about what happens when people use AI under controlled, simulated real-world conditions.
• Focus: Unlike traditional AI evaluations that often rely on probabilities or model performance metrics like accuracy, ARIA emphasizes the direct observation of AI system behaviors and their potential impacts on users. It aims to understand how risks materialize through human-AI interactions.
• Methodology (Three-Level Testbed): ARIA employs a structured three-level testbed to explore risks and impacts:
→ Model testing: Automated tests to confirm claimed AI capabilities and limitations, with a focus on risks rather than just performance accuracy.
→ Red teaming: Adversarial interactions where users attempt to induce risks and identify vulnerabilities in AI applications.
→ Field testing: Examination of both positive and negative impacts that arise during the regular use of AI systems in simulated real-world scenarios.
• Assessment and Measurement: After testing, ARIA's assessment layer involves trained annotators judging user-AI dialogues and analyzing post-session surveys to determine if and how risks materialized. The measurement layer then uses the Contextual Robustness Index (CoRIx), a multidimensional instrument designed to measure an AI system's ability to maintain its functionality in various real-world contexts and user expectations. The CoRIx integrates both quantitative outputs and qualitative judgments.
Summary:
The NIST AI RMF provides the strategic and organizational blueprint for managing AI risks, while NIST ARIA is a tactical program focused on empirically measuring and understanding those risks as they manifest in real-world interactions. ARIA generates the data and methods that can then inform and enhance the broader principles and practices outlined in the AI RMF.
Personal Update
The most effective way to learn a new concept is by comparing and contrasting it against an existing concept. The best way to do this is by a tabular visual representation. A few weeks into Substack I realize there is no easy and straightforward way to insert a table 😱