This paper introduces RiskCards, a framework for structured assessment and
documentation of risks associated with an application of language models. As
with all language, text generated by language models can be harmful, or used to
bring about harm. Automating language generation adds both an element of scale
and also more subtle or emergent undesirable tendencies to the generated text.
Prior work establishes a wide variety of language model harms to many different
actors: existing taxonomies identify categories of harms posed by language
models; benchmarks establish automated tests of these harms; and documentation
standards for models, tasks and datasets encourage transparent reporting.
However, there is no risk-centric framework for documenting the complexity of a
landscape in which some risks are shared across models and contexts, while
others are specific, and where certain conditions may be required for risks to
manifest as harms. RiskCards address this methodological gap by providing a
generic framework for assessing the use of a given language model in a given
scenario. Each RiskCard makes clear the routes for the risk to manifest harm,
their placement in harm taxonomies, and example prompt-output pairs. While
RiskCards are designed to be open-source, dynamic and participatory, we present
a "starter set" of RiskCards taken from a broad literature survey, each of
which details a concrete risk presentation. Language model RiskCards initiate a
community knowledge base which permits the mapping of risks and harms to a
specific model or its application scenario, ultimately contributing to a
better, safer and shared understanding of the risk landscape.

RiskCards 是一种针对语言模型应用的风险评估框架，通过提供清晰的风险路径、在众多风险分类中的美站、以及示例提示 - 输出对，为特定模型或应用场景制定风险和伤害的映射图，最终有助于风险景观的维护和易用性。