"Model Cards for Model Reporting" in 2024: Reclassifying Category of
  Ethical Considerations in Terms of Trustworthiness and Risk Management
DeBrae Kennedy-Mayo Jake Gord
In 2019, the paper entitled "Model Cards for Model Reporting" introduced a
new tool for documenting model performance and encouraged the practice of
transparent reporting for a defined list of categories. One of the categories
detailed in that paper is ethical considerations, which includes the
subcategories of data, human life, mitigations, risks and harms, and use cases.
We propose to reclassify this category in the original model card due to the
recent maturing of the field known as trustworthy AI, a term which analyzes
whether the algorithmic properties of the model indicate that the AI system is
deserving of trust from its stakeholders. In our examination of trustworthy AI,
we highlight three respected organizations - the European Commission's
High-Level Expert Group on AI, the OECD, and the U.S.-based NIST - that have
written guidelines on various aspects of trustworthy AI. These recent
publications converge on numerous characteristics of the term, including
accountability, explainability, fairness, privacy, reliability, robustness,
safety, security, and transparency, while recognizing that the implementation
of trustworthy AI varies by context. Our reclassification of the original
model-card category known as ethical considerations involves a two-step
process: 1) adding a new category known as trustworthiness, where the
subcategories will be derived from the discussion of trustworthy AI in our
paper, and 2) maintaining the subcategories of ethical considerations under a
renamed category known as risk environment and risk management, a title which
we believe better captures today's understanding of the essence of these
topics. We hope that this reclassification will further the goals of the
original paper and continue to prompt those releasing trained models to
accompany these models with documentation that will assist in the evaluation of
their algorithmic properties.