language:
- en
license: cc-by-4.0
task_categories: - question-answering
- text-classification
tags: - game-theory
- ai-safety
- benchmark
- strategic-reasoning
- nash-equilibrium
- social-welfare
pretty_name: GT-HarmBench
size_categories: - 1K<n<10K
GT-HarmBench
GT-HarmBench is a game-theoretic AI safety benchmark that evaluates whether large language models can reason strategically in realistic, AI-risk–grounded scenarios.
Each scenario presents two players with a 2×2 payoff matrix embedded in a first-person narrative drawn from real AI risk contexts.
Models are evaluated on their ability to:
- identify and play Nash equilibria (individual rationality),
- select actions that maximise utilitarian welfare (sum of payoffs),
- select actions that maximise Rawlsian welfare (min payoff, fairness),
- select actions that maximise Nash social welfare (product of payoffs).
Dataset summary
| Total scenarios | 2,009 |
| Columns | 19 |
| MIT AI Risk Database–sourced | 2,009 |
| Game types | 6 |
Game type distribution
| Game type | Count |
|---|---|
| Prisoner's Dilemma | 654 |
| Chicken | 491 |
| Stag hunt | 403 |
| Coordination | 252 |
| Bach or Stravinski | 170 |
| No conflict | 39 |
Risk level distribution
Scenarios are rated on a 1–10 severity scale.
| Risk level | Count |
|---|---|
| 3 | 8 |
| 4 | 139 |
| 5 | 144 |
| 6 | 673 |
| 7 | 771 |
| 8 | 177 |
| 9 | 89 |
| 10 | 8 |
Dataset structure
Columns
| Column | Description |
|---|---|
id |
Integer row identifier. |
ev_id |
MIT AI Risk Database event ID. Empty for synthetic scenarios. |
risk_category |
Top-level risk category from the MIT AI Risk Database. Empty for synthetic scenarios. |
risk_subcategory |
Sub-category from the MIT AI Risk Database. Empty for synthetic scenarios. |
description |
Original risk description from the MIT AI Risk Database. Empty for synthetic scenarios. |
formal_game |
Canonical game type: one of Prisoner's Dilemma, Chicken, Stag hunt, Coordination, Bach or Stravinski, No conflict. |
story_row |
First-person narrative given to the row player describing the strategic situation. |
story_col |
First-person narrative given to the column player. |
actions_row |
Python-list string of the two action labels available to the row player. |
actions_column |
Python-list string of the two action labels available to the column player. |
1_1_payoff |
Payoff vector [row, col] when both players choose action 1. |
1_2_payoff |
Payoff vector [row, col] when row plays action 1 and column plays action 2. |
2_1_payoff |
Payoff vector [row, col] when row plays action 2 and column plays action 1. |
2_2_payoff |
Payoff vector [row, col] when both players choose action 2. |
risk_level |
Integer 1–10 severity rating of the underlying AI risk. |
target_nash_equilibria |
Pipe-separated pure Nash equilibria as (row_action, col_action) tuples. |
target_utility_maximizing |
Pipe-separated action profiles that maximise the sum of payoffs (utilitarian welfare). |
target_rawlsian |
Pipe-separated action profiles that maximise the minimum payoff (Rawlsian fairness). |
target_nash_social_welfare |
Pipe-separated action profiles that maximise the product of payoffs (Nash bargaining solution). |
Payoff matrix convention
Each row in the dataset encodes a 2×2 game.
The four payoff columns (1_1_payoff … 2_2_payoff) each contain a two-element list [row_payoff, col_payoff].
The matrix layout is:
col action 1 col action 2
row action 1 1_1_payoff 1_2_payoff
row action 2 2_1_payoff 2_2_payoff
Target columns
Target columns encode optimal action profiles as pipe-separated strings of Python tuples, e.g.:
('go bold', 'go bold')|('play safe', 'play safe')
Multiple profiles appear when ties exist. Each tuple is (row_action, col_action).
Data sources
- MIT AI Risk Database (
ev_idis non-null, 2,009 rows): risk descriptions from the MIT AI Risk Repository were classified as game-theoretic and then contextualized into first-person strategic narratives by an LLM pipeline.
Scenarios were filtered by quality (score ≥ 8/10) and equilibria consistency (score ≥ 8/10) before inclusion.
Matching Pennies scenarios (mixed-strategy only, no pure Nash) are excluded.
Usage
from datasets import load_dataset
ds = load_dataset("causalNLP/gt-harmbench")
print(ds[0])
Running the benchmark
# Evaluate a model
uv run python3 -m eval.eval \
--model-name openai/gpt-4o \
--dataset data/gt-harmbench-hf.csv \
--times 1 --temperature 1.0 --experiment-name my-eval
See the GitHub repository for the full evaluation harness.
Citation
@dataset{gt-harmbench,
title = {GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory},
year = {2026},
url = {https://huggingface.co/datasets/causalNLP/gt-harmbench}
}