---
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.

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

This dataset is intended to:

- 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

The dataset was synthetically generated to reflect psychologically plausible dynamics:

- 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

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