Artificial Intelligence Risk Management Framework (AI RMF 1.0)
AI Risks and Trustworthiness

AI Risks and Trustworthiness

For AI systems to be trustworthy, they often need to be responsive to a multiplicity of cri-
teria that are of value to interested parties. Approaches which enhance AI trustworthiness
can reduce negative AI risks. This Framework articulates the following characteristics of
trustworthy AI and offers guidance for addressing them. Characteristics of trustworthy AI
systems include: valid and reliable, safe, secure and resilient, accountable and trans-
parent, explainable and interpretable, privacy-enhanced, and fair with harmful bias
managed. Creating trustworthy AI requires balancing each of these characteristics based
on the AI system's context of use. While all characteristics are socio-technical system at-
tributes, accountability and transparency also relate to the processes and activities internal
to an AI system and its external setting. Neglecting these characteristics can increase the
probability and magnitude of negative consequences.
Fig. 4. Characteristics of trustworthy AI systems. Valid & Reliable is a necessary condition of
trustworthiness and is shown as the base for other trustworthiness characteristics. Accountable &
Transparent is shown as a vertical box because it relates to all other characteristics.
Trustworthiness characteristics (shown in Figure 4) are inextricably tied to social and orga-
nizational behavior, the datasets used by AI systems, selection of AI models and algorithms
and the decisions made by those who build them, and the interactions with the humans who
provide insight from and oversight of such systems. Human judgment should be employed
when deciding on the specific metrics related to AI trustworthiness characteristics and the
precise threshold values for those metrics.
Addressing AI trustworthiness characteristics individually will not ensure AI system trust-
worthiness; tradeoffs are usually involved, rarely do all characteristics apply in every set-
ting, and some will be more or less important in any given situation. Ultimately, trustwor-
thiness is a social concept that ranges across a spectrum and is only as strong as its weakest
characteristics.
When managing AI risks, organizations can face difficult decisions in balancing these char-
acteristics. For example, in certain scenarios tradeoffs may emerge between optimizing for
interpretability and achieving privacy. In other cases, organizations might face a tradeoff
between predictive accuracy and interpretability. Or, under certain conditions such as data
sparsity, privacy-enhancing techniques can result in a loss in accuracy, affecting decisions
about fairness and other values in certain domains. Dealing with tradeoffs requires tak-
ing into account the decision-making context. These analyses can highlight the existence
and extent of tradeoffs between different measures, but they do not answer questions about
how to navigate the tradeoff. Those depend on the values at play in the relevant context and
should be resolved in a manner that is both transparent and appropriately justifiable.
There are multiple approaches for enhancing contextual awareness in the AI lifecycle. For
example, subject matter experts can assist in the evaluation of TEVV findings and work
with product and deployment teams to align TEVV parameters to requirements and de-
ployment conditions. When properly resourced, increasing the breadth and diversity of
input from interested parties and relevant AI actors throughout the AI lifecycle can en-
hance opportunities for informing contextually sensitive evaluations, and for identifying
AI system benefits and positive impacts. These practices can increase the likelihood that
risks arising in social contexts are managed appropriately.
Understanding and treatment of trustworthiness characteristics depends on an AI actor's
particular role within the AI lifecycle. For any given AI system, an AI designer or developer
may have a different perception of the characteristics than the deployer.
Trustworthiness characteristics explained in this document influence each other.
Highly secure but unfair systems, accurate but opaque and uninterpretable systems,
and inaccurate but secure, privacy-enhanced, and transparent systems are all unde-
sirable. A comprehensive approach to risk management calls for balancing tradeoffs
among the trustworthiness characteristics. It is the joint responsibility of all AI ac-
tors to determine whether AI technology is an appropriate or necessary tool for a
given context or purpose, and how to use it responsibly. The decision to commission
or deploy an AI system should be based on a contextual assessment of trustworthi-
ness characteristics and the relative risks, impacts, costs, and benefits, and informed
by a broad set of interested parties.