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claude-code - 💡(How to fix) Fix [BUG] Model defaults to male pronouns when referring to people in technical contexts [1 comments, 2 participants]

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anthropics/claude-code#54748•Fetched 2026-04-30 06:37:06
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Error Message

Error Messages/Logs

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Preflight Checklist

  • I have searched existing issues and this hasn't been reported yet
  • This is a single bug report (please file separate reports for different bugs)
  • I am using the latest version of Claude Code

What's Wrong?

When summarizing a colleague's technical input (e.g., ML architecture discussion), Claude defaults to "he/him" pronouns even when no gender information is available. This is especially pronounced for names that don't map to Western gendered naming conventions.

What Should Happen?

Claude should default to "they/them" or use the person's name when pronouns are unknown.

Error Messages/Logs

Steps to Reproduce

Steps to reproduce

  1. Ask Claude to summarize or paraphrase input from a person whose gender is not specified
  2. The context should be technical (software engineering, ML, etc.)
  3. Claude will often default to "he/him" rather than "they/them"

Claude Model

None

Is this a regression?

No, this never worked

Last Working Version

No response

Claude Code Version

2.1.119

Platform

Anthropic API

Operating System

macOS

Terminal/Shell

Terminal.app (macOS)

Additional Information

Why this matters

  • Misgenders colleagues, especially women and non-binary people in engineering
  • Reinforces the stereotype that engineers are male by default
  • The bias is stronger in technical contexts, suggesting occupational stereotyping from training data

#54127

extent analysis

TL;DR

Update the Claude Code model to use "they/them" pronouns by default when gender information is unknown.

Guidance

  • Review the training data for occupational stereotyping and bias, which may be causing the model to default to "he/him" pronouns in technical contexts.
  • Consider implementing a fallback to use the person's name when pronouns are unknown, to avoid misgendering.
  • Evaluate the model's performance on a diverse set of names and contexts to identify and address any remaining biases.
  • Collaborate with the development team to prioritize this fix, given the potential impact on misgendering colleagues and reinforcing stereotypes.

Example

No code snippet is provided, as the issue is related to model training data and bias, rather than a specific code implementation.

Notes

The fix may require updates to the model's training data, algorithms, or configuration, and may involve collaboration with the development team and experts in bias mitigation.

Recommendation

Apply a workaround by using a custom implementation to override the model's default pronoun usage, until a permanent fix is available. This is necessary to address the potential harm caused by the model's current behavior.

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