Proposal to insert a sentence in para. 2.2.1 clarifying that decision trees can be learned from data and not all are manually specified rule systems, and to modify para. 2.2.5 to state that Generative AI models can use different architectures, with some being Transformer-based and potentially foundation or general-purpose AI models.
Proposal to modify the definition of concept drift in para. 3.2.9.2 and Annex 2, para. 6.2 to clarify that concept drift arises when changes alter the relationship between inputs and target outputs, and that a change in input distribution alone does not necessarily imply a change in this relationship.
Proposal to modify the definition of Validation Data to replace ‘a final’ with ‘candidate’ and append clarification that it is used for model selection or tuning while independent test data used for final performance reporting are not used for model selection or tuning, and add a definition of Test Data as data used to evaluate the final selected model and not used for fitting, model selection or tuning, with a note on dataset separation to avoid leakage between sets.
Proposal to modify Annex 1, 1.1.2 to replace the clause beginning ‘and such applications fall within the scope’ with language clarifying that applications fall within the first mode of AI use only where AI is part of the regulated product and directly performs or supports a safety-relevant function, and to add that AI tools used solely for development or assessment fall under section 2.3.3.
Proposal to add working definitions for uncertainty quantification, aleatoric uncertainty, epistemic uncertainty, and uncertainty-aware action at the end of section 1.3 of AI-11-03, Annex 5. These definitions do not replace definitions in applicable standards and support general discussion of uncertainty and its effects on action.
Proposal to append sentences to paragraph 2 addressing uncertainty-related risks propagated from upstream to downstream AI modules and supporting uncertainty-aware action, and to paragraph 3 considering whether uncertainty information remains meaningful when used by downstream AI modules and how it affects the resulting action.
Proposal to add text to para. 3.2.4.6 clarifying that uncertainty-related risks may propagate from upstream to downstream AI modules when uncertainty information is inadequately communicated or used, affecting the resulting action, and to add text to Annex 2, item 1.6 clarifying that inadequate communication or use of uncertainty information across AI modules can propagate or amplify uncertainty-related risks and affect the resulting action, even when the upstream estimate is informative.