NIST AI Risk Management Framework Playbook

AI systems are socio-technical in nature and can have positive, neutral, or negative implications that extend beyond their stated purpose. Negative impacts can be wide- ranging and affect individuals, groups, communities, organizations, and society, as well as the environment and national security.

Organizations can create a baseline for system monitoring to increase opportunities for detecting emergent risks. After an AI system is deployed, engaging different stakeholder groups – who may be aware of, or experience, benefits or negative impacts that are unknown to AI actors involved in the design, development and deployment activities – allows organizations to understand and monitor system benefits and potential negative impacts more readily.

• Establish and document stakeholder engagement processes at the earliest stages of system formulation to identify potential impacts from the AI system on individuals, groups, communities, organizations, and society.
• Employ methods such as value sensitive design (VSD) to identify misalignments between organizational and societal values, and system implementation and impact.
• Identify approaches to engage, capture, and incorporate input from system end users and other key stakeholders to assist with continuous monitoring for potential impacts and emergent risks.
• Incorporate quantitative, qualitative, and mixed methods in the assessment and documentation of potential impacts to individuals, groups, communities, organizations, and society.
• Identify a team (internal or external) that is independent of AI design and development functions to assess AI system benefits, positive and negative impacts and their likelihood.
• Evaluate and document stakeholder feedback to assess potential impacts for actionable insights regarding trustworthiness characteristics and changes in design approaches and principles.
• Develop TEVV procedures that incorporate socio-technical elements and methods and plan to normalize across organizational culture. Regularly review and refine TEVV processes.

Organizations can document the following:

• If the AI system relates to people, does it unfairly advantage or disadvantage a particular social group? In what ways? How was this managed?
• If the AI system relates to other ethically protected groups, have appropriate obligations been met? (e.g., medical data might include information collected from animals)
• If the AI system relates to people, could this dataset expose people to harm or legal action? (e.g., financial social or otherwise) What was done to mitigate or reduce the potential for harm?

AI Transparency Resources:

• Datasheets for Datasets. URL
• GAO-21-519SP: AI Accountability Framework for Federal Agencies & Other Entities. URL
• AI policies and initiatives, in Artificial Intelligence in Society, OECD, 2019. URL
• Intel.gov: AI Ethics Framework for Intelligence Community - 2020. URL
• Assessment List for Trustworthy AI (ALTAI) - The High-Level Expert Group on AI - 2019. LINK, URL

Susanne Vernim, Harald Bauer, Erwin Rauch, et al. 2022. A value sensitive design approach for designing AI-based worker assistance systems in manufacturing. Procedia Comput. Sci. 200, C (2022), 505–516. URL

Harini Suresh and John V. Guttag. 2020. A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. arXiv:1901.10002. Retrieved from URL

Margarita Boyarskaya, Alexandra Olteanu, and Kate Crawford. 2020. Overcoming Failures of Imagination in AI Infused System Development and Deployment. arXiv:2011.13416. URL

Konstantinia Charitoudi and Andrew Blyth. A Socio-Technical Approach to Cyber Risk Management and Impact Assessment. Journal of Information Security 4, 1 (2013), 33-41. URL

Raji, I.D., Smart, A., White, R.N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.

Emanuel Moss, Elizabeth Anne Watkins, Ranjit Singh, Madeleine Clare Elish, & Jacob Metcalf. 2021. Assemlbing Accountability: Algorithmic Impact Assessment for the Public Interest. Data & Society. Accessed 7/14/2022 at URL

Shari Trewin (2018). AI Fairness for People with Disabilities: Point of View. ArXiv, abs/1811.10670. URL

Ada Lovelace Institute. 2022. Algorithmic Impact Assessment: A Case Study in Healthcare. Accessed July 14, 2022. URL

Microsoft Responsible AI Impact Assessment Template. 2022. Accessed July 14, 2022. URL

Microsoft Responsible AI Impact Assessment Guide. 2022. Accessed July 14, 2022. URL

Microsoft Responsible AI Standard, v2. URL

Microsoft Research AI Fairness Checklist. URL

PEAT AI & Disability Inclusion Toolkit – Risks of Bias and Discrimination in AI Hiring Tools. URL