NIST AI RMF Playbook
AI RMF Playbook > Map > MAP 1.1 (part 1)

MAP 1.1: Intended purpose, potentially beneficial uses, context-specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented. Considerations include: specific set or types of users along with their expectations; potential positive and negative impacts of system uses to individuals, communities, organizations, society, and the planet; assumptions and related limitations about AI system purposes; uses and risks across the development or product AI lifecycle; TEVV and system metrics.

About:
Highly accurate and optimized systems can cause harm. Relatedly, organizations should expect broadly deployed AI tools to be reused, repurposed, and potentially misused regardless of intentions.

AI actors can work collaboratively, and with external parties such as community groups, to help delineate the bounds of acceptable deployment, consider preferable alternatives, and identify principles and strategies to manage likely risks. Context mapping is the first step in this effort, and may include examination of the following:

* intended purpose and impact of system use.
* concept of operations.
* intended, prospective, and actual deployment setting.
* requirements for system deployment and operation.
* end user and operator expectations.
* specific set or types of end users.
* potential negative impacts to individuals, groups, communities, organizations, and society - or context-specific impacts such as legal requirements or impacts to the environment.
* unanticipated, downstream, or other unknown contextual factors.
* how AI system changes connect to impacts.

These types of processes can assist AI actors in understanding how limitations, constraints, and other realities associated with the deployment and use of AI technology can create impacts once they are deployed or operate in the real world. When coupled with the enhanced organizational culture resulting from the established policies and procedures in the Govern function, the Map function can provide opportunities to foster and instill new perspectives, activities, and skills for approaching risks and impacts.

Context mapping also includes discussion and consideration of non-AI or non-technology alternatives especially as related to whether the given context is narrow enough to manage AI and its potential negative impacts. Non-AI alternatives may include capturing and evaluating information using semi-autonomous or mostly-manual methods.

Suggested Actions:
- Maintain awareness of industry, technical, and applicable legal standards.
- Examine trustworthiness of AI system design and consider, non-AI solutions
- Consider intended AI system design tasks along with unanticipated purposes in collaboration with human factors and socio-technical domain experts.
- Define and document the task, purpose, minimum functionality, and benefits of the AI system to inform considerations about whether the utility of the project or its lack of.
- Identify whether there are non-AI or non-technology alternatives that will lead to more trustworthy outcomes.
- Examine how changes in system performance affect downstream events such as decision-making (e.g: changes in an AI model objective function create what types of impacts in how many candidates do/do not get a job interview).
- Determine actions to map and track post-decommissioning stages of AI deployment and potential negative or positive impacts to individuals, groups and communities.
- Determine the end user and organizational requirements, including business and technical requirements.
- Determine and delineate the expected and acceptable AI system context of use, including:
- social norms
- Impacted individuals, groups, and communities
- potential positive and negative impacts to individuals, groups, communities, organizations, and society
- operational environment
- 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.
- Gain and maintain awareness about evaluating scientific claims related to AI system performance and benefits before launching into system design.
- Identify human-AI interaction and/or roles, such as whether the application will support or replace human decision making.
- Plan for risks related to human-AI configurations, and document requirements, roles, and responsibilities for human oversight of deployed systems.

Transparency and Documentation:
### Organizations can document the following
- To what extent is the output of each component appropriate for the operational context?
- Which AI actors are responsible for the decisions of the AI and is this person aware of the intended uses and limitations of the analytic?
- Which AI actors are responsible for maintaining, re-verifying, monitoring, and updating this AI once deployed?
- Who is the person(s) accountable for the ethical considerations across the AI lifecycle?

### AI Transparency Resources
- GAO-21-519SP: AI Accountability Framework for Federal Agencies & Other Entities, [URL](https://www.gao.gov/products/gao-21-519sp)
- "Stakeholders in Explainable AI," Sep. 2018. [URL](http://arxiv.org/abs/1810.00184)
- "Microsoft Responsible AI Standard, v2". [URL](https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE4ZPmV)

References:
### Socio-technical systems

Andrew D. Selbst, danah boyd, Sorelle A. Friedler, et al. 2019. Fairness and Abstraction in Sociotechnical Systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT'19). Association for Computing Machinery, New York, NY, USA, 59-68. [URL](https://doi.org/10.1145/3287560.3287598)

### Problem formulation