Artificial Intelligence Risk Management Framework (AI RMF 1.0)
AI RMF Core > Subcategories > GOVERN 6.2

GOVERN 6.2: Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be high-risk. Categories Subcategories 5.2 Map The MAP function establishes the context to frame risks related to an AI system. The AI lifecycle consists of many interdependent activities involving a diverse set of actors (See Figure 3). In practice, AI actors in charge of one part of the process often do not have full visibility or control over other parts and their associated contexts. The interdependencies between these activities, and among the relevant AI actors, can make it difficult to reliably anticipate impacts of AI systems. For example, early decisions in identifying purposes and objectives of an AI system can alter its behavior and capabilities, and the dynamics of deployment setting (such as end users or impacted individuals) can shape the impacts of AI system decisions. As a result, the best intentions within one dimension of the AI lifecycle can be undermined via interactions with decisions and conditions in other, later activities. This complexity and varying levels of visibility can introduce uncertainty into risk management practices. Anticipating, assessing, and otherwise addressing potential sources of negative risk can mitigate this uncertainty and enhance the integrity of the decision process. The information gathered while carrying out the MAP function enables negative risk prevention and informs decisions for processes such as model management, as well as an initial decision about appropriateness or the need for an AI solution. Outcomes in the MAP function are the basis for the MEASURE and MANAGE functions. Without contextual knowledge, and awareness of risks within the identified contexts, risk management is difficult to perform. The MAP function is intended to enhance an organization's ability to identify risks and broader contributing factors. Implementation of this function is enhanced by incorporating perspectives from a diverse internal team and engagement with those external to the team that developed or deployed the AI system. Engagement with external collaborators, end users, potentially impacted communities, and others may vary based on the risk level of a particular AI system, the makeup of the internal team, and organizational policies. Gathering such broad perspectives can help organizations proactively prevent negative risks and develop more trustworthy AI systems by: • improving their capacity for understanding contexts; • checking their assumptions about context of use; • enabling recognition of when systems are not functional within or out of their intended context; • identifying positive and beneficial uses of their existing AI systems; • improving understanding of limitations in AI and ML processes; • identifying constraints in real-world applications that may lead to negative impacts; • identifying known and foreseeable negative impacts related to intended use of AI systems; and • anticipating risks of the use of AI systems beyond intended use. After completing the MAP function, Framework users should have sufficient contextual knowledge about AI system impacts to inform an initial go/no-go decision about whether to design, develop, or deploy an AI system. If a decision is made to proceed, organizations should utilize the MEASURE and MANAGE functions along with policies and procedures put into place in the GOVERN function to assist in AI risk management efforts. It is incumbent on Framework users to continue applying the MAP function to AI systems as context, capabilities, risks, benefits, and potential impacts evolve over time. Practices related to mapping AI risks are described in the NIST AI RMF Playbook. Table 2 lists the MAP function's categories and subcategories. Table 2: Categories and subcategories for the MAP function. MAP 1: Context is established and understood.