---
library_name: transformers
tags:
- text-classification
- ai-governance
- risk-classification
- nlp
- roberta
- eu-ai-act
license: mit
language:
- en
---

# 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


[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## 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

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

[More Information Needed]

## 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

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### 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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

[More Information Needed]

## 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.