Datasets:

Dyra1204
/

human_ai_trust_demo

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:

pip install datasets

Load the dataset:

from datasets import load_dataset

ds = load_dataset("Dyra1204/human_ai_trust_demo")

Access individual fields:

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 metric:

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