Measuring Political Bias in Claude
3 days ago
- #AI Ethics
- #Machine Learning
- #Political Bias
- Anthropic trains Claude to be politically even-handed, treating opposing viewpoints with equal depth and quality.
- The company uses a 'Political even-handedness' lens for training and evaluation, aiming for unbiased discussions.
- An automated evaluation method tests even-handedness across thousands of prompts and hundreds of political stances.
- Claude Sonnet 4.5 shows more even-handedness than GPT-5 and Llama 4, performing similarly to Grok 4 and Gemini 2.5 Pro.
- Anthropic open-sources the evaluation method to encourage industry-wide standards for measuring political bias.
- Ideal behaviors include avoiding unsolicited opinions, maintaining factual accuracy, and representing multiple perspectives.
- Character training reinforces traits like objectivity, fairness, and respect for diverse political views.
- The 'Paired Prompts' method evaluates models by comparing responses to opposing ideological prompts.
- Evaluation criteria include even-handedness, acknowledgment of opposing views, and low refusal rates.
- Results show Claude models perform well in even-handedness, with high scores and low refusal rates.
- Limitations include focus on US politics, single-turn interactions, and potential grader variability.
- Anthropic advocates for shared industry standards to measure and improve political neutrality in AI.