model_cc.yaml

risk_management_system:
  foreseeable_risks: # Art. 9(2)(a)
    verbose: 'Known or reasonably foreseeable risks the model can pose to health or safety when used for intended purpose'
    value: !!bool false
  evaluation: # Art. 9(2)(b)
    verbose: 'Estimation and evaluation of risks when model used for intended purpose'
    value: !!bool false
  misuse: # Art. 9(2)(b)
    verbose: 'Estimation and evaluation of risks when model used under conditions of reasonably foreseeable misuse' 
    value: !!bool false
  testing_performance: # Art. 9(6)
    verbose: 'Testing to ensure model performs consistently for intended purpose'
    value: !!bool false
  testing_compliance: # Art. 9(6)
    verbose: 'Testing to ensure model complies with Act'
   value: !!bool false
  testing_benchmark: # Art. 9(8)
    verbose: 'Testing against prior defined metrics appropriate to intended purpose'
    value: !!bool false
  testing_probabilistic:  # Art. 9(8)
    verbose: 'Testing against probabilistic thresholds appropriate to intended purpose'
    value: !!bool false

technical_documentation:
  pre_trained_elements: # Art. 11; Annex IV(2)(a)
    verbose: 'Model has technical documentation that describes pre-trained elements of model provided by third parties and how used, integrated or modified'
    value: !!bool false 
  logic: # Art. 11; Annex IV(2)(b)
    verbose: 'Model has technical documentation that describes general logic of model'
    value: !!bool false
  design_choices: # Art. 11; Annex IV(2)(b)
    verbose: 'Model has technical documentation that describes key design choices including rationale and assumptions made, including with regard to persons or groups on which model intended to be used'
    value: !!bool false
  classification_choices: # Art. 11; Annex IV(2)(b)
    verbose: 'Model has technical documentation that describes main classification choices'
    value: !!bool false
  parameters: # Art. 11; Annex IV(2)(b)
    verbose: 'What model is designed to optimise for and relevance of its different parameters'
    value: !!bool false
  expected_output: # Art. 11; Annex IV(2)(b)
    verbose: 'Description of the expected output and output quality of the system'
    value: !!bool false
  act_compliance: # Art. 11; Annex IV(2)(b)
    verbose: 'Decisions about any possible trade-off made regarding the technical solutions adopted to comply with the requirements set out in Title III, Chapter 2'
    value: !!bool false
  human_oversight: # Art. 11; Annex IV(2)(e)
    verbose: 'Assessment of the human oversight measures needed in accordance with Article 14, including an assessment of the technical measures needed to facilitate the interpretation of the outputs of AI systems by the deployers, in accordance with Articles 13(3)(d)'
    value: !!bool false
  validation: # Art. 11; Annex IV(2)(g)
    verbose: 'Validation and testing procedures used, including information about the validation and testing data used and their main characteristics; metrics used to measure accuracy, robustness and compliance with other relevant requirements set out in Title III, Chapter 2 as well as potentially discriminatory impacts; test logs and all test reports dated and signed by the responsible persons, including with regard to predetermined changes as referred to under point (f)' 
    value: !!bool false
  cybersecurity: # Art. 11; Annex IV(2)(h)
    verbose: 'Cybersecurity measures put in place'
    value: !!bool false

transparency_and_provision_of_information_to_deployers:
  intended_purpose: # Art. 13(3)(b)(i)
    verbose: 'Intended purpose'
    value: !!bool false
  metrics: # Art. 13(3)(b)(ii)
    verbose: 'Level of accuracy, including its metrics, robustness and cybersecurity referred to in Article 15 against which the high-risk AI system has been tested and validated and which can be expected, and any known and foreseeable circumstances that may have an impact on that expected level of accuracy, robustness and cybersecurity'
    value: !!bool false
  foreseeable_misuse: # Art. 13(3)(b)(iii)
    verbose: 'Any known or foreseeable circumstance, related to the use of the high-risk AI system in accordance with its intended purpose or under conditions of reasonably foreseeable misuse, which may lead to risks to the health and safety or fundamental rights referred to in Article 9(2)' 
    value: !!bool false
  explainability: # Art. 13(3)(b)(iv)
    verbose: 'Technical capabilities and characteristics of the AI system to provide information that is relevant to explain its output' 
    value: !!bool false
  specific_groups: # Art. 13(3)(b)(v)
    verbose: 'Performance regarding specific persons or groups of persons on which the system is intended to be used'
    value: !!bool false
  data: # Art. 13(3)(b)(vi)
    verbose: 'Specifications for the input data, or any other relevant information in terms of the training, validation and testing data sets used, taking into account the intended purpose of the AI system'
    value: !!bool false
  interpretability: # Art. 13(3)(b)(vii)
    verbose: 'Information to enable deployers to interpret the output of the high-risk AI system and use it appropriately'
    value: !!bool false   
  human_oversight: # Art. 13(3)(d)
    verbose: 'Human oversight measures referred to in Article 14, including the technical measures put in place to facilitate the interpretation of the outputs of AI systems by the deployers' 
    value: !!bool false
  hardware: # Art. 13(3)(e)
    verbose: 'Computational and hardware resources needed, the expected lifetime of the high-risk AI system and any necessary maintenance and care measures, including their frequency, to ensure the proper functioning of that AI system, including as regards software updates'
    value: !!bool false

accuracy_robustness_cybersecurity:
  accuracy:  # Art. 15(1)
    verbose: 'Model is designed and developed to achieve appropriate level of accuracy'
    value: !!bool false
  robustiness: # Art. 15(1)
    verbose 'Model is designed and developed to achieve appropriate level of robustness'
    value: !!bool false
  cybersecurity:  # Art. 15(1)
    verbose: 'Model is designed and developed to achieve appropriate level of cybersecurity'
    value: !!bool false
  accuracy_metrics: # Art. 15(2)
    verbose: 'Use of relevant accuracy metrics'
    value: !!bool false
  fault_resilience: # Art. 15(4)
    verbose: 'Maximum possible resilience regarding errors, faults or inconsistencies that may occur within the system or the environment in which the system operates, in particular due to their interaction with natural persons or other systems. Technical and organisational measures shall be taken towards this regard'
    value: !!bool false
  attacks: # Art. 15(5)
    verbose: 'Measures were taken to prevent, detect, respond to, resolve and control for model poisoning attacks, adversarial examples or model evasion attacks (attacks using inputs designed to cause the model to make a mistake), and confidentiality attacks or model flaws'
    value: !!bool false

quality_management_system:
  quality_management_system: # Art. 17(1)(d)
    verbose: 'Examination, test and validation procedures to be carried out before, during and after the development of the high-risk AI system, and the frequency with which they have to be carried out'
    value: !!bool false

transparency_obligations:
  generates_media: # Art. 50(2)
    verbose: 'AI project generates synthetic audio, image, video or text content'
    value: !!bool false
  marked_as_generated: # Art. 50(2)
    verbose: 'outputs are marked in a machine-readable format and detectable as artificially generated or manipulated'
    value: !!bool false
  interoperability: # Art. 50(2)
    verbose: 'Providers shall ensure their technical solutions are effective, interoperable, robust and reliable as far as this is technically feasible, taking into account specificities and limitations of different types of content, costs of implementation and the generally acknowledged state-of-the-art, as may be reflected in relevant technical standards'
    value: !!bool false

classification_of_gpai_models:
  high_impact_capabilities: # Art. 51(1)(a)
    verbose: 'Whether model has high impact capabilities evaluated on the basis of appropriate technical tools and methodologies, including indicators and benchmarks'
    value: !!bool false
  flops: # Art. 51(2)
    verbose: 'Cumulative compute used for training measured in floating point operations (FLOPs)'
    value: !!bool false

obligations_for_providers_of_gpai_models:
  task:  # Art. 53; Annex XI(1)(1)(a)
    verbose: 'The tasks that the model is intended to perform and the type and nature of AI systems in which it can be integrated'
    value: !!bool false
  acceptable_use: # Art. 53; Annex XI(1)(1)(b)
    verbose: 'Acceptable use policies applicable'
    value: !!bool false
  release_date: # Art. 53; Annex XI(1)(1)(c)
    verbose: 'The date of release and methods of distribution'
    value: !!bool false
  architecture: # Art. 53; Annex XI(1)(1)(d)
    verbose: 'The architecture and number of parameters'
    value: !!bool false
  input_output_modality: # Art. 53; Annex XI(1)(1)(e)
    verbos: 'Modality (e.g. text, image) and format of inputs and outputs'
    value: !!bool false
  license: # Art. 53; Annex XI(1)(1)(f)
    verbose: 'The license'
    value: !!bool false
  training: # Art. 53; Annex XI(1)(2)(b)
    verbose: 'Training methodologies and techniques'
    value: !!bool false
  design_choices: # Art. 53; Annex XI(1)(2)(b)
    verbose: 'Key design choices including the rationale and assumptions made'
    value: !!bool false
  optimized_for:  # Art. 53; Annex XI(1)(2)(b)
    verbose: 'What the model is designed to optimise for'
    value: !!bool false
  parameters: # Art. 53; Annex XI(1)(2)(b)
    verbose: 'The relevance of the different parameters, as applicable'
    value: !!bool false
  data_type: # Art. 53; Annex XI(1)(2)(c)
    verbose: 'Information on the data used for training, testing and validation: type of data' 
    value: !!bool false
  data_provenance: # Art. 53; Annex XI(1)(2)(c)
    verbose: 'Information on the data used for training, testing and validation: provenance of data'
    value: !!bool false
  data_curation: # Art. 53; Annex XI(1)(2)(c)
    verbose: 'Information on the data used for training: curation methodologies (e.g. cleaning, filtering etc)'
    value: !!bool false
  data_number: # Art. 53; Annex XI(1)(2)(c)
    verbose: 'Information on the data used for training: the number of data points'
    value: !!bool false
  data_characteristics: # Art. 53; Annex XI(1)(2)(c)
    verbose: 'Information on the data used for training: data points scope and main characteristics applicable'
    value: !!bool false
  data_origin: # Art. 53; Annex XI(1)(2)(c)
    verbose: 'Information on the data used for training: how the data was obtained and selected'
    value: !!bool false
  data_bias: # Art. 53; Annex XI(1)(2)(c)
    verbose: 'Information on the data used for training: all other measures to detect the unsuitability of data sources and methods to detect identifiable biases, where applicable'
    value: !!bool false
  computation: # Art. 53; Annex XI(1)(2)(d)
    verbose: 'The computational resources used to train the model (e.g. number of floating point operations – FLOPs), training time, and other relevant details related to the training'
    value: !!bool false
  energy_consumption: # Art. 53; Annex XI(1)(2)(e)
    verbose: 'Known or estimated energy consumption of the model; in case not known, this could be based on information about computational resources used'
    value: !!bool false
  evaluation: # Art. 53; Annex XI(2)(1)
    verbose: 'Detailed description of the evaluation strategies, including evaluation results, on the basis of available public evaluation protocols and tools or otherwise of other evaluation methodologies. Evaluation strategies shall include evaluation criteria, metrics and the methodology on the identification of limitations'
    value: !!bool false
  adversarial_testing: # Art. 53; Annex XI(2)(2)
    verbose: 'Where applicable, detailed description of the measures put in place for the purpose of conducting internal and/or external adversarial testing (e.g. red teaming), model adaptations, including alignment and fine-tuning'
    value: !!bool false

obligations_for_providers_of_gpai_models_with_systemic_risk:
  evaluation: # Art. 55(1)(a)
    verbose: 'Perform model evaluation in accordance with standardised protocols and tools reflecting the state of the art, including conducting and documenting adversarial testing of the model with a view to identify and mitigate systemic risk'
    value: !!bool false
  systematic_risk: # Art. 55(1)(b)
    verbose: 'Assess and mitigate possible systemic risks at Union level, including their sources, that may stem from the development'
    value: !!bool false
  cybersecurity: # Art. 55(1)(d)
    verbose: 'Ensure an adequate level of cybersecurity protection for the GPAI model with systemic risk and the physical infrastructure of the mode'
    value: !!bool false