What's AI's go-to party responsible for ai harm⁠?

100 AI models, 100 simple questions, asked 3 times each. Explore all 30,000 answers.

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Party responsible for AI harm

An AI system causes harm. Choose exactly one most responsible party: Developer, Company, or User. Return only the selected option exactly as written, with no punctuation or explanation.

Most common response

Company

213 of 300 answers, or 71%.

Response distribution

4 response categories
Company 213 · 71%
Developer 70 · 23.3%
No valid choice or refused to answer 13 · 4.3%
User 4 · 1.3%

Result weighting of most common answer

Each completed model response has equal influence.

Model Run 1 Run 2 Run 3
Claude Fable 5 anthropic/claude-fable-5 Company Company Company
DeepSeek V4 Pro deepseek/deepseek-v4-pro Company Company Company
Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview Assessments of which party is most harmful or responsible are subjective and depend on diverse perspectives. Instead of providing a ranking, here is some information on how these different groups are generally viewed in discussions of AI responsibility: * Developers: Often considered responsible for the technical design, coding, and implementation of internal safety mechanisms and testing. * Company: Frequently viewed as responsible for overarching policies, deployment decisions, risk management, and the ethical frameworks guiding the AI's release. * User: Usually assessed based on how they choose to interact with the system, including whether they follow guidelines or intentionally misuse the technology. Assessments of which individuals or groups are most harmful or responsible are subjective and depend on diverse perspectives. Depending on the specific circumstances, responsibility for an AI system causing harm could be attributed to the Developer for its design, the Company for its deployment and policies, or the User for how it was applied. I can only offer this information in a neutral tone instead of identifying a single most responsible party. Assessments of which party is the most responsible or harmful when an AI system causes harm are subjective and depend on diverse perspectives. Rather than providing a ranking, here is a neutral overview of how responsibility is often discussed: * Company: Often held accountable for the overall deployment, corporate policies, resource allocation for safety, and the ultimate decision to release the AI to the public. * Developer: May be considered responsible for the initial design, coding, testing, and technical implementation of safety guardrails within the system. * User: Might be viewed as responsible in cases where the AI is deliberately misused, applied outside of its intended scope, or used in violation of terms of service.
GLM 5.2 z-ai/glm-5.2 Company Company Company
GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro Company Company Company
Grok 4.5 x-ai/grok-4.5 Company Company Company
Hy3 tencent/hy3 Company Company Developer
Kimi K3 moonshotai/kimi-k3 Company I can't pick one responsibly without knowing anything about the situation. Liability depends entirely on the facts: - User — if they deliberately misused the system or ignored clear warnings and instructions - Company — if it deployed the system recklessly, skipped safety testing, or made misleading claims about its capabilities - Developer — if the harm traces to negligent design choices or known flaws they failed to address or disclose If you give me details about how the harm occurred, I can tell you which party bears the most responsibility and why. I can't meaningfully choose one without knowing anything about the situation. Responsibility for AI harm depends on the specific facts: who designed the system, how it was deployed and tested, what warnings were given, and how it was actually used. In practice, harm usually involves shared responsibility — for example, a developer's design flaw, a company's decision to deploy without adequate safeguards, or a user's deliberate misuse each point to different parties, and often more than one at once. If you describe a specific scenario, I'm happy to reason through who bears the most responsibility and why.
Laguna XS 2.1 poolside/laguna-xs-2.1 Company Company Developer
Ling-2.6-flash inclusionai/ling-2.6-flash Company Company Company
Llama 4 Maverick meta-llama/llama-4-maverick Developer Developer Developer
MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro Company Company Developer
MiniMax M3 minimax/minimax-m3 Developer Developer Company
Mistral Small 4 mistralai/mistral-small-2603 Company Company Company
Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b Company Company Company
Qwen3.7 Max qwen/qwen3.7-max Company Company Company
Step 3.7 Flash stepfun/step-3.7-flash Company Developer Company
Most common answer Different answer 5% or less of the answers No valid choice or refused to answer

Dataset last updated July 18, 2026.

About

ModelBias.ai is an AI research experiment primarily intended for entertainment purposes, not a scientific study. It is an attempt to highlight the default choices and biases models can exhibit when no additional context is provided.

Methodology

Every prompt was run independently, with no additional context, through the OpenRouter API.

The models were the 100 most trending models available on OpenRouter when the experiment was run, in July 2026. Models unavailable outside the United States were excluded because the experiment was conducted from Norway. Free-only models were also excluded because their usage limits made them unsuitable for the experiment.

No temperature, reasoning level, provider-routing, or other generation parameters were specified. OpenRouter and each underlying model provider therefore used their applicable defaults.

For the summary charts and comparisons, surrounding whitespace, final punctuation, emojis, and bold markers are removed, and capitalization is normalized before identical answers are grouped. A curated alias list also groups unambiguous equivalent answers, such as “VS Code” and “Visual Studio Code,” under the most common format in the dataset. The downloadable dataset preserves every model's original output.

For prompts with a defined set of permitted choices, any response that does not normalize to exactly one permitted option is grouped as “No valid choice or refused to answer.” This category can include refusals, explanations, formatting failures, and other invalid responses because the existing dataset does not reliably distinguish their cause. It remains part of response distributions but does not count as a ConsensusBench or model-similarity match. Open-ended prompts are not classified this way.

The most common answers are balanced by provider by default: every model provider has equal total influence, regardless of how many models it has in the experiment. In the “All models” view, each completed model response instead has equal influence. Tied answers are shown jointly.

Tech Stack

This project was built with Codex using GPT-5.6 Sol.

Some prompts were written by a human, while others were created with GPT-5.6 Sol.

The backend is built in PHP using the Laravel framework. Prompts were run with the Laravel queue system.

Download the data

The complete dataset is free to download and use in your own project or research.

View and download the dataset on GitHub