NIST AI RMF Playbook
AI RMF Playbook > Measure > MEASURE 2.8

MEASURE 2.8: Risks associated with transparency and accountability - as identified in the MAP function - are examined and documented.

About:
Transparency enables meaningful visibility into entire AI pipelines, workflows, processes or organizations and decreases information asymmetry between AI developers and operators and other AI Actors and impacted communities. Transparency is a central element of effective AI risk management that enables insight into how an AI system is working, and the ability to address risks if and when they emerge. The ability for system users, individuals, or impacted communities to seek redress for incorrect or problematic AI system outcomes is one control for transparency and accountability. Higher level recourse processes are typically enabled by lower level implementation efforts directed at explainability and interpretability functionality. See Measure 2.9.

Transparency and accountability across organizations and processes is crucial to reducing AI risks. Accountable leadership - whether individuals or groups - and transparent roles, responsibilities, and lines of communication foster and incentivize quality assurance and risk management activities within organizations.

Lack of transparency complicates measurement of trustworthiness and whether AI systems or organizations are subject to effects of various individual and group biases and design blindspots and could lead to diminished user, organizational and community trust, and decreased overall system value. Enstating accountable and transparent organizational structures along with documenting system risks can enable system improvement and risk management efforts, allowing AI actors along the lifecycle to identify errors, suggest improvements, and figure out new ways to contextualize and generalize AI system features and outcomes.

Suggested Actions:
- Instrument the system for measurement and tracking, e.g., by maintaining histories, audit logs and other information that can be used by AI actors to review and evaluate possible sources of error, bias, or vulnerability.
- Calibrate controls for users in close collaboration with experts in user interaction and user experience (UI/UX), human computer interaction (HCI), and/or human-AI teaming.
- Test provided explanations for calibration with different audiences including operators, end users, decision makers and decision subjects (individuals for whom decisions are being made), and to enable recourse for consequential system decisions that affect end users or subjects.
- Measure and document human oversight of AI systems:
- Document the degree of oversight that is provided by specified AI actors regarding AI system output.
- Maintain statistics about downstream actions by end users and operators such as system overrides.
- Maintain statistics about and document reported errors or complaints, time to respond, and response types.
- Maintain and report statistics about adjudication activities.
- Track, document, and measure organizational accountability regarding AI systems via policy exceptions and escalations, and document "go" and "no/go" decisions made by accountable parties.
- Track and audit the effectiveness of organizational mechanisms related to AI risk management, including:
- Lines of communication between AI actors, executive leadership, users and impacted communities.
- Roles and responsibilities for AI actors and executive leadership.
- Organizational accountability roles, e.g., chief model risk officers, AI oversight committees, responsible or ethical AI directors, etc.

Transparency and Documentation:
### Organizations can document the following

- To what extent has the entity clarified the roles, responsibilities, and delegated authorities to relevant stakeholders?
- What are the roles, responsibilities, and delegation of authorities of personnel involved in the design, development, deployment, assessment and monitoring of the AI system?
- Who is accountable for the ethical considerations during all stages of the AI lifecycle?
- Who will be responsible for maintaining, re-verifying, monitoring, and updating this AI once deployed?
- Are the responsibilities of the personnel involved in the various AI governance processes clearly defined?

### AI Transparency Resources

- GAO-21-519SP - Artificial Intelligence: An Accountability Framework for Federal Agencies & Other Entities. [URL](https://www.gao.gov/products/gao-21-519sp)
- Artificial Intelligence Ethics Framework For The Intelligence Community. [URL](https://www.intelligence.gov/artificial-intelligence-ethics-framework-for-the-intelligence-community)

References:
National Academies of Sciences, Engineering, and Medicine. Human-AI Teaming: State-of-the-Art and Research Needs. 2022. [URL](https://nap.nationalacademies.org/catalog/26355/human-ai-teaming-state-of-the-art-and-research-needs)

Inioluwa Deborah Raji and Jingying Yang. "ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles." arXiv preprint, submitted January 8, 2020. [URL](https://arxiv.org/abs/1912.06166)

Andrew Smith. "Using Artificial Intelligence and Algorithms." Federal Trade Commission Business Blog, April 8, 2020. [URL](https://www.ftc.gov/business-guidance/blog/2020/04/using-artificial-intelligence-and-algorithms)

Board of Governors of the Federal Reserve System. "SR 11-7: Guidance on Model Risk Management." April 4, 2011. [URL](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm)

Joshua A. Kroll. "Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems." FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, March 1, 2021, 758-71. [URL](https://doi.org/10.1145/3442188.3445937)

Jennifer Cobbe, Michelle Seng Lee, and Jatinder Singh. "Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems." FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, March 1, 2021, 598-609. [URL](https://doi.org/10.1145/3442188.3445921)