983 lines
30 KiB
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983 lines
30 KiB
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Roadmap
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class=""
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<span>Example of Use Cases</span>
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</a>
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<span>Crosswalk Documents</span>
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li.usa-nav__submenu-item::marker {
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<a href="/airmf-resources/">AI RMF Resources</a>
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sb-menu
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<span class="caret"></span>
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<span class="caret"></span>
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id="ddtoggle_18"
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<span>Playbook</span>
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<span class="caret"></span>
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</a>
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<a href="/airmf-resources/roadmap/"
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class="usa-current"
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>
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<span>Roadmap</span>
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</a>
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<li class="usa-sitenav__item ">
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<a href="/airmf-resources/usecases/"
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class=""
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>
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<span>Example of Use Cases</span>
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</a>
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</li>
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<li class="usa-sitenav__item ">
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<a href="/airmf-resources/crosswalks/"
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class=""
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>
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<span>Crosswalk Documents</span>
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</a>
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</li>
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</ul>
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</li>
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<li class="usa-sidenav__item">
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<a href="/glossary/"
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class=""
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>
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<span>Glossary</span>
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</a>
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</li>
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<li class="usa-sidenav__item">
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<a href="/technical-reports/"
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class=""
|
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>
|
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<span>Technical Reports</span>
|
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|
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</a>
|
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</li>
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</ul>
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</aside>
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<div class="usa-in-page-nav-container site-in-page-nav-container">
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<main id="main-content" class="main-content">
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<div class="grid-container"><div class="grid-container usa-prose">
|
||
<h1>Roadmap</h1>
|
||
|
||
<p class="usa-intro subtitle">Roadmap for the NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0)</p>
|
||
|
||
<p>
|
||
This Roadmap is a companion to the Artificial Intelligence Risk Management Framework (AI RMF 1.0). As directed by
|
||
the National Artificial Intelligence Initiative Act of 2020 (P.L. 116-283), the goal of the AI RMF is to offer a
|
||
<em>voluntary</em> resource to the organizations designing, developing, deploying, or using AI systems to help
|
||
manage the many risks of AI and promote trustworthy and responsible development and use of AI systems.
|
||
</p>
|
||
|
||
<p>
|
||
The Framework is intended to be voluntary, rights-preserving, non-sector specific, and use-case agnostic, providing
|
||
flexibility to organizations of all sizes and in all sectors and throughout society to implement the approaches in
|
||
the Framework. The AI RMF is designed to equip organizations and individuals – AI actors – with approaches that
|
||
increase the trustworthiness of AI systems.
|
||
</p>
|
||
|
||
<p>
|
||
This Roadmap identifies key activities for advancing the AI RMF that
|
||
<em>could be carried out by NIST in collaboration with private and public sector organizations – or by those
|
||
organizations independently</em>. NIST’s involvement will depend in part on resources available. Work described in
|
||
the Roadmap is intended to help
|
||
fill gaps in knowledge, practice, or guidance and be useful to a broader audience in pursuit of trustworthy and
|
||
responsible AI. Roadmap activities will change as AI technologies and experience evolve.
|
||
</p>
|
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|
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<div id="roadmap--feedback-box" class="usa-alert usa-alert--info">
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<div class="usa-alert__body">
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<h4 class="usa-alert__heading">Community feedback</h4>
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Comments on this Roadmap are welcomed by NIST at any time and may refer to specific items that are either
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missing or incomplete, or express commitments to pursue Roadmap items. Comments should be addressed to
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<a href="mailto:AIframework@nist.gov" class="usa-link"><i class="fa-regular fa-envelope"></i>
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AIframework@nist.gov</a>.
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</p>
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</div>
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</div>
|
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|
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<p>Below are areas that NIST considers to be top priorities to advance the AI RMF and its use:</p>
|
||
|
||
<ol class="usa-list">
|
||
<li>
|
||
<strong>Alignment with international standards and production crosswalks</strong>
|
||
<strong>to related standards</strong>. (e.g., ISO/IEC 5338, ISO/IEC 38507, ISO/IEC 22989, ISO/IEC 24028, ISO/IEC
|
||
DIS 42001, and ISO/IEC NP 42005.) <br /><br />In its role as federal AI standards coordinator, NIST works across
|
||
the government and with industry stakeholders to identify critical standards development activities, strategies,
|
||
and gaps. Based on priorities outlined in the NIST-developed “<a
|
||
href="https://www.nist.gov/document/report-plan-federal-engagement-developing-technical-standards-and-related-tools"
|
||
title="Report: A Plan for Federal Engagement in Developing Technical Standards and Related Tools"
|
||
class="usa-link">Plan for Federal Engagement in AI Standards and Related Tools</a>,” NIST is tracking AI
|
||
standards development opportunities, periodically collecting and analyzing information
|
||
about agencies’ AI standards-related priority activities, and making recommendations through the interagency
|
||
process to optimize engagement. NIST also participates in selected AI standards activities. See:
|
||
<a href="https://www.nist.gov/artificial-intelligence/technical-ai-standards"
|
||
class="usa-link">https://www.nist.gov/artificial-intelligence/technical-ai-standards</a><br />
|
||
|
||
</li>
|
||
<li>
|
||
<strong>Expanded TEVV efforts.</strong> NIST will work with the broader community to develop tools, benchmarks,
|
||
testbeds, and standardized methodologies for evaluating risks in AI and system trustworthiness, including from a
|
||
socio-technical lens.<br />
|
||
|
||
</li>
|
||
<li>
|
||
<strong>AI RMF 1.0 Profiles</strong>. Profiles are a primary method for organizations to share real-life examples
|
||
of how they put the AI RMF into practice. These profiles can be for a given industry sector (such as hiring,
|
||
criminal justice, lending), cross-sectoral (such as large language models, cloud-based services or acquisition),
|
||
temporal (such as current vs desired state), or other topics. Organizations and individuals are encouraged to
|
||
jointly or independently produce AI RMF profiles.<br />
|
||
|
||
</li>
|
||
<li>
|
||
<strong>Guidance on the tradeoffs and relationships that may exist among trustworthiness characteristics</strong>.
|
||
A key challenge in managing AI risks is the need to balance and navigate tradeoffs between trustworthiness
|
||
characteristics, and to do so systematically and dependent on the values at play in the variety of contexts and
|
||
use cases. NIST intends to investigate key concepts in this topic area and develop guidance for navigating
|
||
tradeoffs with the broader community.<br />
|
||
|
||
</li>
|
||
<li>
|
||
<strong>Measuring the effectiveness of the AI RMF. </strong>As the AI RMF is adopted, organizations will have
|
||
different experiences applying the Framework. These lessons and insights can improve the utility and effectiveness
|
||
of the AI RMF and enhance our understanding of managing AI risks more generally. NIST will collaborate with
|
||
experts in program evaluation, the AI RMF user community, and other interested parties to establish methods to
|
||
capture, evaluate, and share insights about the Framework’s use. These efforts will contribute to revisions to AI
|
||
RMF 1.0 and future versions of this Roadmap.<br />
|
||
|
||
</li>
|
||
<li>
|
||
<strong>Development of case studies</strong> about how the AI RMF has been used by a single organization or
|
||
sector, context, or AI actor. Use cases may be similar to AI RMF profiles but can more directly describe
|
||
organizational experiences and challenges using the AI RMF – and how they were addressed – along with information
|
||
about resources, timeframes, and AI RMF effectiveness.<br />
|
||
|
||
</li>
|
||
<li>
|
||
<strong>Guidance on human factors and human-AI teaming in the context of AI risk management.</strong> NIST intends
|
||
to investigate how human-AI teams should best be configured to reduce likelihood of negative impacts/harms to
|
||
individuals, groups, communities, and society. These impacts may be related to human<em> insights</em> about
|
||
AI-produced output, or human <em>oversight</em> of AI systems and their operation in real world
|
||
environments.<br />
|
||
|
||
</li>
|
||
<li>
|
||
<strong>Guidance related to explainability and interpretability, and how to apply that guidance within the AI RMF.
|
||
</strong>As our knowledge advances about how AI is understood across different audiences and for different
|
||
purposes, NIST
|
||
has identified a need to connect the field of AI explainability and interpretability more directly to AI risk
|
||
management.<br />
|
||
|
||
</li>
|
||
<li>
|
||
<strong>Guidance on methods for developing reasonable risk tolerances. </strong>NIST will work with the community
|
||
to identify approaches organizations can use to develop risk tolerances.<br />
|
||
|
||
</li>
|
||
<li>
|
||
<strong>Tutorials and other</strong>
|
||
<strong>resources to enhance multi-disciplinary and socio-technical approaches to AI risk management.
|
||
</strong>Topics associated with AI risk management are complex and evolving, and the community of interest will
|
||
continue
|
||
to expand and become increasingly multidisciplinary. To keep the broader AI community informed, NIST will
|
||
encourage subject matter experts and other interested parties to develop education materials targeted to different
|
||
audience types and will contribute its technical expertise to these efforts.
|
||
</li>
|
||
</ol>
|
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