| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - human-ai-interaction |
| - trust |
| - uncertainty |
| - belief-updating |
| - interpretability |
| task_categories: |
| - text-classification |
| --- |
| |
| # Human–AI Trust & Belief Dynamics (Demo Dataset) |
|
|
| This dataset is a small, synthetic but theory-grounded benchmark designed to support human-centered evaluation of AI systems under uncertainty. |
|
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| It accompanies the `human_ai_trust` metric in Hugging Face Evaluate. |
|
|
| --- |
|
|
| ## What This Dataset Contains |
|
|
| Each row represents a single human–AI interaction instance with the following fields: |
|
|
| - `prediction`: model prediction (binary) |
| - `reference`: ground truth label |
| - `confidence`: model confidence in its prediction |
| - `human_trust`: human trust rating in the model output |
| - `belief_prior`: user's belief before seeing the AI output |
| - `belief_posterior`: user's belief after seeing the AI output |
| - `explanation_length`: proxy for explanation complexity |
|
|
| --- |
|
|
| ## What This Dataset Is For |
|
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| This dataset is intended to: |
|
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| - Demonstrate the `human_ai_trust` evaluation metric |
| - Support research on: |
| - trust calibration |
| - belief updating |
| - uncertainty communication |
| - explanation–confidence alignment |
| - Provide a lightweight benchmark for HCI and HCAI experiments |
|
|
| --- |
|
|
| ## How to Use |
|
|
| Install the `datasets` library if you haven't already: |
| ```bash |
| pip install datasets |
| ``` |
|
|
| Load the dataset: |
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("Dyra1204/human_ai_trust_demo") |
| ``` |
|
|
| Access individual fields: |
| ```python |
| predictions = ds["train"]["prediction"] |
| references = ds["train"]["reference"] |
| confidences = ds["train"]["confidence"] |
| human_trust = ds["train"]["human_trust"] |
| belief_prior = ds["train"]["belief_prior"] |
| belief_posterior = ds["train"]["belief_posterior"] |
| ``` |
|
|
| Use it with the companion [`human_ai_trust`](https://github.com/dyra-12/evaluate/blob/main/metrics/human_ai_trust/README.md) metric: |
| ```python |
| import evaluate |
| |
| metric = evaluate.load("human_ai_trust") |
| |
| results = metric.compute( |
| predictions=ds["train"]["prediction"], |
| references=ds["train"]["reference"], |
| confidence=ds["train"]["confidence"], |
| human_trust=ds["train"]["human_trust"], |
| belief_prior=ds["train"]["belief_prior"], |
| belief_posterior=ds["train"]["belief_posterior"], |
| ) |
| |
| print(results) |
| ``` |
|
|
| --- |
|
|
| ## What This Dataset Is Not |
|
|
| - It is not a real human-subjects dataset |
| - It is not suitable for training models |
| - It does not capture cultural, demographic, or contextual variation |
| - It does not reflect real medical, legal, or safety-critical decisions |
|
|
| --- |
|
|
| ## How the Data Was Generated |
|
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| The dataset was synthetically generated to reflect psychologically plausible dynamics: |
|
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| - Model confidence is higher for correct predictions |
| - Human trust tracks confidence with noise |
| - Beliefs shift partially toward model confidence |
| - Explanations are longer when confidence is lower |
|
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| This makes it suitable for exercising trust- and belief-based evaluation metrics without requiring human data collection. |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Synthetic data cannot substitute for real behavioral data |
| - Trust and belief dynamics are simplified |
| - Explanation complexity is approximated via length |
| - No domain context is modeled |
|
|
| Users are encouraged to replace this dataset with real human-interaction data for empirical studies. |
|
|
| --- |
|
|
| ## License |
|
|
| MIT |
|
|
