"Model Cards for Model Reporting" in 2024: Reclassifying Category of Ethical Considerations in Terms of Trustworthiness and Risk Management
DeBrae Kennedy-Mayo, Jake Gord

TL;DR
This paper proposes reclassifying ethical considerations in model cards into trustworthiness and risk management categories, aligning documentation with evolving trustworthy AI principles to improve transparency and evaluation.
Contribution
It introduces a new classification framework for model cards, separating trustworthiness from risk management based on recent developments in trustworthy AI guidelines.
Findings
Trustworthiness includes accountability, explainability, fairness, privacy, reliability, robustness, safety, security, transparency.
The reclassification aligns model documentation with current AI trustworthiness standards.
A two-step process for updating model card categories is proposed.
Abstract
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…
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Taxonomy
TopicsSafety Systems Engineering in Autonomy · Risk and Safety Analysis
