ai-risk-classifier-roberta

AI Risk Classifier (RoBERTa)

AI use case classifier that distinguishes between informational use, decision influence, automated decision-making, and potentially harmful AI practices. This model was selected after experimenting with a DistilBERT baseline.

Model Details

This model classifies AI use cases into risk categories inspired by EU AI governance concepts.

It is designed to distinguish how AI systems impact people, based on their role in decision-making processes.

The model categorizes AI use cases into four classes:

• Lower Risk (informational use)
• Possible High Risk (decision influence)
• Likely High Risk (decision impact)
• Potentially Prohibited (harmful or unfair practices)

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: BKlein Digital Labs
• Funded by [optional]: Self-funded (independent project)
• Shared by [optional]: BKlein Digital Labs
• Model type: Text classification (sequence classification)
• Language(s) (NLP): English
• License: MIT
• Finetuned from model [optional]: roberta-base

Model Sources [optional]

• Base model: roberta-base

• Task: text classification

• Framework: Hugging Face Transformers

• Repository: [More Information Needed]

• Paper [optional]: [More Information Needed]

• Demo [optional]: [More Information Needed]

Intended Use

This model is intended for:

• exploring AI governance concepts
• educational purposes
• early-stage risk assessment of AI use cases

Example input:

"A system ranks insurance claims for staff review."

Example output:

Possible High Risk

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias and Risks

Limitations

• The dataset is relatively small and curated manually
• The model may not generalize to all real-world scenarios
• It does not capture full legal or regulatory complexity
• Predictions should not be used for compliance decisions

Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

The model was trained on a manually designed dataset (~90 use cases) covering domains such as:

• finance
• healthcare
• employment
• public sector

Special focus was placed on defining clear boundaries between:

• informational use
• decision influence
• automated decision-making
• harmful or unfair practices [More Information Needed]

Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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• Hours used: [More Information Needed]
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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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Disclaimer

This model provides indicative classification for educational purposes only. It does not replace legal advice or formal regulatory assessment.